Triple
T18886145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara De Fina |
E461960
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object |
Tom Gilroy
Tom Gilroy is an American filmmaker, writer, and actor known for his work in independent cinema and theater.
|
E1348966
|
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: Tom Gilroy | Statement: [Barbara De Fina, collaboratedWith, Tom Gilroy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Gilroy Context triple: [Barbara De Fina, collaboratedWith, Tom Gilroy]
-
A.
Walt Garrison
Walt Garrison was an American football fullback for the Dallas Cowboys and a celebrated rodeo cowboy known for his toughness and dual-sport career.
-
B.
Glen Tullman
Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
-
C.
Ted Daughety
Ted Daughety is an American physician and pulmonologist best known as the husband of Kansas Governor Laura Kelly.
-
D.
Robert George Brett
Robert George Brett was a Canadian physician and politician who served as the second Lieutenant Governor of Alberta in the early 20th century.
-
E.
Tom McGuire
Tom McGuire was an early 20th-century film actor known for his roles in silent and early sound comedies, including ensemble appearances in major studio productions.
- 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: Tom Gilroy Triple: [Barbara De Fina, collaboratedWith, Tom Gilroy]
Generated description
Tom Gilroy is an American filmmaker, writer, and actor known for his work in independent cinema and theater.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Gilroy Target entity description: Tom Gilroy is an American filmmaker, writer, and actor known for his work in independent cinema and theater.
-
A.
Walt Garrison
Walt Garrison was an American football fullback for the Dallas Cowboys and a celebrated rodeo cowboy known for his toughness and dual-sport career.
-
B.
Glen Tullman
Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
-
C.
Ted Daughety
Ted Daughety is an American physician and pulmonologist best known as the husband of Kansas Governor Laura Kelly.
-
D.
Robert George Brett
Robert George Brett was a Canadian physician and politician who served as the second Lieutenant Governor of Alberta in the early 20th century.
-
E.
Tom McGuire
Tom McGuire was an early 20th-century film actor known for his roles in silent and early sound comedies, including ensemble appearances in major studio productions.
- 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_69d8dcfc3430819095ee6fc0eb4c06a5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c4760d808190b502c4ed1f24424c |
completed | April 20, 2026, 6:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0582b37de881908475c69461c15c8f |
completed | May 14, 2026, 8:07 a.m. |
| NEDg | Description generation | batch_6a05887693d081908fa60534c3c36144 |
completed | May 14, 2026, 8:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05891f4a248190a5527a939fcb16da |
completed | May 14, 2026, 8:34 a.m. |
Created at: April 10, 2026, 11:57 a.m.