Triple
T20183503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anaconda (1997 film) |
E492792
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Luis Llosa |
—
|
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: Luis Llosa | Statement: [Anaconda (1997 film), director, Luis Llosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luis Llosa Context triple: [Anaconda (1997 film), director, Luis Llosa]
-
A.
Luis Llosa
chosen
Luis Llosa is a Peruvian film director and producer best known internationally for helming Hollywood action and thriller films in the 1990s.
-
B.
Luis Esquivel
Luis Esquivel is a person notable enough to be recognized as a significant bearer of the surname Esquivel.
-
C.
García Morte
García Morte is the family name of Spanish actor Álvaro Morte, internationally known for his role as "The Professor" in the series Money Heist.
-
D.
Horacio Marquínez
Horacio Marquínez is a cinematographer known for his work on the film "L.I.E."
-
E.
Jorge Ibargüengoitia
Jorge Ibargüengoitia was a Mexican novelist, playwright, and satirist known for his sharp humor and critical portrayals of Mexican society and politics.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668f068748190a0941e98ef5afd59 |
completed | April 20, 2026, 5:57 p.m. |
Created at: April 11, 2026, 11:36 p.m.