Triple
T1391872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bacon |
E29977
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Kevin Bacon |
E74573
|
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: Kevin Bacon | Statement: [Bacon, hasNotableBearer, Kevin Bacon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Bacon Context triple: [Bacon, hasNotableBearer, Kevin Bacon]
-
A.
Kevin Bacon
chosen
Kevin Bacon is an American actor and producer known for his versatile film and television roles and for inspiring the pop-culture concept of "Six Degrees of Kevin Bacon."
-
B.
James Woods
James Woods is an American actor known for his intense performances in film and television, including acclaimed roles in movies such as "Salvador," "Videodrome," and "Casino."
-
C.
John Goodman
John Goodman is an American actor known for his roles in the sitcom "Roseanne," numerous Coen brothers films, and for voicing Sulley in Pixar's "Monsters, Inc." franchise.
-
D.
Thomas Haden Church
Thomas Haden Church is an American actor known for roles in the TV series "Wings" and films such as "Sideways" and "Spider-Man 3."
-
E.
Matthew Broderick
Matthew Broderick is an American actor known for his work in film, theater, and television, particularly for iconic roles in movies like "Ferris Bueller's Day Off" and "WarGames."
- 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_69a498dc92f8819094a1108f8ac90f43 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c35f7ab081909fe81dd475d6196f |
completed | March 1, 2026, 10:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acde28dd888190baa4a26f96f33e0a |
completed | March 8, 2026, 2:25 a.m. |
Created at: March 1, 2026, 7:59 p.m.