Triple
T17990748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Child 44 |
E430362
|
entity |
| Predicate | filmAdaptationDirector |
P255
|
FINISHED |
| Object | Daniel Espinosa |
—
|
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 Espinosa | Statement: [Child 44, filmAdaptationDirector, Daniel Espinosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Espinosa Context triple: [Child 44, filmAdaptationDirector, Daniel Espinosa]
-
A.
Daniel Espinosa
chosen
Daniel Espinosa is a Swedish film director known for action and thriller films such as "Safe House" and "Life."
-
B.
Rodrigo Prieto
Rodrigo Prieto is a renowned Mexican cinematographer known for his visually distinctive work on major films by directors such as Martin Scorsese and Alejandro G. Iñárritu.
-
C.
Fernando Argüelles
Fernando Argüelles is a cinematographer known for his work on films such as "Doppelganger."
-
D.
Miguel Ordóñez
Miguel Ordóñez is an illustrator known for his playful, minimalist artwork in children’s books and other visual storytelling projects.
-
E.
Jorge Saralegui
Jorge Saralegui is a film producer best known for his work on genre movies, including the horror adaptation "Queen of the Damned."
- 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_69d8b90364248190a37381adea932f42 |
completed | April 10, 2026, 8:46 a.m. |
| NER | Named-entity recognition | batch_69e4b29f127c81908b0c4cb3787e002c |
completed | April 19, 2026, 10:46 a.m. |
Created at: April 10, 2026, 10:23 a.m.