Triple
T19474207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Fang (1991 film) |
E487201
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Robert Dalva |
—
|
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: Robert Dalva | Statement: [White Fang (1991 film), editedBy, Robert Dalva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robert Dalva Context triple: [White Fang (1991 film), editedBy, Robert Dalva]
-
A.
Robert Dalva
chosen
Robert Dalva is an American film editor best known for his acclaimed work on movies such as "The Black Stallion."
-
B.
Paul Gervais
Paul Gervais was a French painter known for his large-scale decorative works and allegorical murals in prominent public buildings.
-
C.
Robert Tessier
Robert Tessier was an American character actor and stuntman known for his imposing, bald, tough-guy roles in action and exploitation films of the 1960s and 1970s.
-
D.
Louis-Philippe Demers
Louis-Philippe Demers was a Canadian lawyer and politician who served as a Liberal member of the House of Commons in the early 20th century.
-
E.
Francois-Eric Gendron
François-Éric Gendron is a French actor known for his work in film, television, and theater, often appearing in French and international productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633ef69508190b0d71ef663ba8977 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:39 p.m.