Triple
T22764023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Seuss film adaptations |
E563071
|
entity |
| Predicate | notableDirector |
P4744
|
FINISHED |
| Object | Chris Renaud |
—
|
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: Chris Renaud | Statement: [Dr. Seuss film adaptations, notableDirector, Chris Renaud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Renaud Context triple: [Dr. Seuss film adaptations, notableDirector, Chris Renaud]
-
A.
Chris Renaud
chosen
Chris Renaud is an American animator and film director best known for co-directing popular animated features such as Despicable Me and The Lorax.
-
B.
Hugo Gélin
Hugo Gélin is a French film director and screenwriter known for his popular, emotionally driven comedies and dramas.
-
C.
Dany Boon
Dany Boon is a French comedian, actor, and filmmaker best known for his popular comedy films such as "Bienvenue chez les Ch'tis."
-
D.
Pierre Larquey
Pierre Larquey was a prolific French character actor known for his numerous supporting roles in French cinema from the 1930s to the 1950s.
-
E.
Michel Zitt
Michel Zitt is a prominent French scholar in scientometrics and research evaluation, recognized internationally for his influential contributions to the quantitative study of science and technology.
- 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17a7f56848190b5e90f9916a5b349 |
completed | April 29, 2026, 3:26 a.m. |
Created at: April 17, 2026, 3:26 p.m.