Triple
T13744378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop |
E330175
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Mike Bishop
Mike Bishop is an American politician and attorney who served as a U.S. Representative from Michigan.
|
E1059130
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mike Bishop | Statement: [Bishop, hasNotableBearer, Mike Bishop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Bishop Context triple: [Bishop, hasNotableBearer, Mike Bishop]
-
A.
Chris Bishop
Chris Bishop is a television producer best known for his work as an executive producer on the ABC sitcom "The Goldbergs."
-
B.
Mike Gillespie
Mike Gillespie was a highly respected American college baseball coach best known for leading the USC Trojans to sustained success, including a national championship.
-
C.
Michael Rogers
Michael Rogers is a relatively common personal name shared by multiple individuals across fields such as politics, sports, and the arts, rather than referring to one singular widely recognized figure.
-
D.
Bob Bishop
Bob Bishop is a fictional character portrayed by actor Stephen Tobolowsky, best known as a morally conflicted high-ranking member of the Company in the television series "Heroes."
-
E.
Mike Talman
Mike Talman is a con man who becomes entangled in a tense scheme involving a blind woman and hidden heroin in the thriller "Wait Until Dark."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mike Bishop Triple: [Bishop, hasNotableBearer, Mike Bishop]
Generated description
Mike Bishop is an American politician and attorney who served as a U.S. Representative from Michigan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mike Bishop Target entity description: Mike Bishop is an American politician and attorney who served as a U.S. Representative from Michigan.
-
A.
Chris Bishop
Chris Bishop is a television producer best known for his work as an executive producer on the ABC sitcom "The Goldbergs."
-
B.
Mike Gillespie
Mike Gillespie was a highly respected American college baseball coach best known for leading the USC Trojans to sustained success, including a national championship.
-
C.
Michael Rogers
Michael Rogers is a relatively common personal name shared by multiple individuals across fields such as politics, sports, and the arts, rather than referring to one singular widely recognized figure.
-
D.
Bob Bishop
Bob Bishop is a fictional character portrayed by actor Stephen Tobolowsky, best known as a morally conflicted high-ranking member of the Company in the television series "Heroes."
-
E.
Mike Talman
Mike Talman is a con man who becomes entangled in a tense scheme involving a blind woman and hidden heroin in the thriller "Wait Until Dark."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de020855ec8190a60fa1cb761f2e68 |
completed | April 14, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a8501890819081152bc2e9c06836 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a994cd688190a077a4854c5c71c9 |
completed | May 3, 2026, 8:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7aa32b8c8819088bbc9e478c21c06 |
completed | May 3, 2026, 8:04 p.m. |
Created at: April 9, 2026, 10:08 p.m.