Triple
T1207712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Man of Steel |
E25926
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Kevin Costner |
E20152
|
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 Costner | Statement: [Man of Steel, starring, Kevin Costner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Costner Context triple: [Man of Steel, starring, Kevin Costner]
-
A.
Kevin Costner
chosen
Kevin Costner is an American actor and filmmaker known for leading roles in films such as "Dances with Wolves," "Field of Dreams," and "The Bodyguard."
-
B.
Joe Costner
Joe Costner is an American actor and the son of Academy Award–winning actor and filmmaker Kevin Costner.
-
C.
Jeff Bridges
Jeff Bridges is an acclaimed American actor known for his versatile performances in films such as "The Big Lebowski," "Crazy Heart," and "True Grit."
-
D.
Kurt Russell
Kurt Russell is an American actor known for his versatile performances in films ranging from action and science fiction to drama and comedy, including iconic roles in movies like "Escape from New York," "The Thing," and "Tombstone."
-
E.
Bill Paxton
Bill Paxton was an American actor and filmmaker known for his versatile roles in films such as "Aliens," "Twister," "Titanic," and "Apollo 13."
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bdc483b481908e5bfef5d2f4fc5a |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8a0b1ad8819098a89585fe6ba090 |
completed | March 7, 2026, 8:26 p.m. |
Created at: March 1, 2026, 7:46 p.m.