Triple
T20080630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Tree of Blood |
E499989
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Daniel Grao |
—
|
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: Daniel Grao | Statement: [The Tree of Blood, castMember, Daniel Grao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Grao Context triple: [The Tree of Blood, castMember, Daniel Grao]
-
A.
Daniel Grao
chosen
Daniel Grao is a Spanish film, television, and stage actor known for his roles in works by director Pedro Almodóvar and various acclaimed Spanish TV series.
-
B.
Daniel Fanego
Daniel Fanego is an Argentine actor known for his extensive work in film, television, and theater.
-
C.
James Royo
James Royo is a music producer best known for his work on Ty Dolla $ign’s album "Beach House 3."
-
D.
John Duarte
John Duarte is a Republican politician and businessman serving as the U.S. Representative for California's 13th congressional district.
-
E.
Daniel Morales
Daniel Morales is the reckless yet highly skilled Marseille taxi driver and protagonist of the French action-comedy Taxi film series.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66557c19c8190b511857490bbd423 |
completed | April 20, 2026, 5:41 p.m. |
Created at: April 11, 2026, 3:41 p.m.