Triple
T22545458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucy Walker |
E557410
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Frank Walker |
—
|
NE NERFINISHED |
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: Frank Walker | Statement: [Lucy Walker, relative, Frank Walker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frank Walker Context triple: [Lucy Walker, relative, Frank Walker]
-
A.
Frank Walker
Frank Walker is a brilliant but disillusioned inventor who serves as the central protagonist in Disney’s science-fiction film "Tomorrowland."
-
B.
Joseph Walker
Joseph Walker was a prominent American cinematographer best known for his influential work during Hollywood’s Golden Age, particularly in collaboration with director Frank Capra.
-
C.
Clark Walker
Clark Walker is a film producer known for his work on the crime drama movie "The Newton Boys."
-
D.
Roy Walker
Roy Walker is the injured Hollywood stuntman whose fantastical storytelling to a young girl drives the narrative of the 2006 film "The Fall."
-
E.
Rob Walker
Rob Walker is a technology entrepreneur best known as a co-founder of the semiconductor company LSI Logic.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69e11e58662081909ae346ab384514ca |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f35b9888190b4e1b50d5097b211 |
completed | April 29, 2026, 1:30 a.m. |
Created at: April 16, 2026, 8:51 p.m.