Triple
T16146319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Corbin |
E391791
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
John Corbin
John Corbin was the husband of American Revolutionary War heroine Margaret Corbin, with whom he served in the Continental Army.
|
E1197281
|
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 Corbin | Statement: [Margaret Corbin, spouse, John Corbin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Corbin Context triple: [Margaret Corbin, spouse, John Corbin]
-
A.
Joel McNeely
Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
-
B.
Danny Donahue
Danny Donahue is a central character in the comedy film "Role Models," serving as one of the key figures around whom the story’s mentorship and personal growth themes revolve.
-
C.
Ken Koblun
Ken Koblun is a Canadian bassist best known for his early involvement with the influential 1960s rock band Buffalo Springfield.
-
D.
John Hoffman
John Hoffman is an American writer, producer, and director best known for co-creating the mystery-comedy television series "Only Murders in the Building."
-
E.
Michael Krieger
Michael Krieger is a fictional character appearing in the story of "Watch Over Me."
- 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 Corbin Triple: [Margaret Corbin, spouse, John Corbin]
Generated description
John Corbin was the husband of American Revolutionary War heroine Margaret Corbin, with whom he served in the Continental Army.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Corbin Target entity description: John Corbin was the husband of American Revolutionary War heroine Margaret Corbin, with whom he served in the Continental Army.
-
A.
Joel McNeely
Joel McNeely is an American composer and conductor best known for his work on film and television scores, including numerous projects for Disney and other major studios.
-
B.
Danny Donahue
Danny Donahue is a central character in the comedy film "Role Models," serving as one of the key figures around whom the story’s mentorship and personal growth themes revolve.
-
C.
Ken Koblun
Ken Koblun is a Canadian bassist best known for his early involvement with the influential 1960s rock band Buffalo Springfield.
-
D.
John Hoffman
John Hoffman is an American writer, producer, and director best known for co-creating the mystery-comedy television series "Only Murders in the Building."
-
E.
Michael Krieger
Michael Krieger is a fictional character appearing in the story of "Watch Over Me."
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9376fc8190bd9ef586b00c1d3b |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff7a59c0481908eb346efaf10a0f6 |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff8cc75b08190a5824dc35b751f93 |
completed | May 10, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff98b3d7c8190bb284321d17f58e2 |
completed | May 10, 2026, 3:20 a.m. |
Created at: April 10, 2026, 5:01 a.m.