Triple
T13052838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabot |
E327489
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Bruce Cabot |
E63264
|
NE FINISHED |
How this triple was built (2 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: Bruce Cabot | Statement: [Cabot, hasNotableBearer, Bruce Cabot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bruce Cabot Context triple: [Cabot, hasNotableBearer, Bruce Cabot]
-
A.
Bruce Cabot
chosen
Bruce Cabot was an American actor best known for his leading role opposite Fay Wray in the classic 1933 adventure-horror film "King Kong."
-
B.
Ben E. Cabell
Ben E. Cabell was an American politician who served as mayor of Dallas, Texas, in the early 20th century.
-
C.
Ben Cafferty
Ben Cafferty is a cynical, sharp-tongued political operative and senior adviser to Selina Meyer on the television series "Veep."
-
D.
Paul Hackett
Paul Hackett is a former U.S. Marine and Iraq War veteran best known for his high-profile 2005 special election campaign as a Democratic congressional candidate in Ohio.
-
E.
Stephen Burbank
Stephen Burbank is a prominent legal scholar and professor known for his expertise in civil procedure and complex litigation at the University of Pennsylvania Law School.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980b98fa081908cfa92116799e874 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ead25b7c8190af2ccf26b44c2ea2 |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 9, 2026, 8:58 p.m.