Triple
T14321305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Flanagan |
E355094
|
entity |
| Predicate | doctoralAdvisor |
P167
|
FINISHED |
| Object |
John Hallowell
John Hallowell was a political scientist and academic known for his work in political theory and for mentoring scholars such as Tom Flanagan.
|
E1093361
|
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: John Hallowell | Statement: [Tom Flanagan, doctoralAdvisor, John Hallowell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Hallowell Context triple: [Tom Flanagan, doctoralAdvisor, John Hallowell]
-
A.
Michael Hoyt
Michael Hoyt is an individual notable enough to be recognized as a prominent bearer of the Hoyt surname.
-
B.
John Crowell
John Crowell is a person notable enough to be recognized as a significant bearer of the surname Crowell.
-
C.
Mark T. Williams
Mark T. Williams is the son of renowned American composer and conductor John Williams.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Philip Brownstein
Philip Brownstein was a professional basketball coach best known for leading the early NBA-era Chicago Stags franchise.
- 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: John Hallowell Triple: [Tom Flanagan, doctoralAdvisor, John Hallowell]
Generated description
John Hallowell was a political scientist and academic known for his work in political theory and for mentoring scholars such as Tom Flanagan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Hallowell Target entity description: John Hallowell was a political scientist and academic known for his work in political theory and for mentoring scholars such as Tom Flanagan.
-
A.
Michael Hoyt
Michael Hoyt is an individual notable enough to be recognized as a prominent bearer of the Hoyt surname.
-
B.
John Crowell
John Crowell is a person notable enough to be recognized as a significant bearer of the surname Crowell.
-
C.
Mark T. Williams
Mark T. Williams is the son of renowned American composer and conductor John Williams.
-
D.
Daniel Ullman
Daniel Ullman was an American screenwriter known for his work on mid-20th-century genre films, particularly Westerns and thrillers.
-
E.
Philip Brownstein
Philip Brownstein was a professional basketball coach best known for leading the early NBA-era Chicago Stags franchise.
- 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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883bf71c8190a9a092a025cf98f0 |
completed | April 14, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd468c4a7c8190951adb28e1d71365 |
completed | May 8, 2026, 2:12 a.m. |
| NEDg | Description generation | batch_69fd478e6644819080692fbca8ec6c80 |
completed | May 8, 2026, 2:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4851528c81909855c0f8484c278e |
completed | May 8, 2026, 2:20 a.m. |
Created at: April 10, 2026, 1:13 a.m.