Triple
T1747682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guillermo del Toro |
E38371
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Marisa del Toro |
E38371
|
NE FINISHED |
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: Marisa del Toro | Statement: [Guillermo del Toro, child, Marisa del Toro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marisa del Toro Context triple: [Guillermo del Toro, child, Marisa del Toro]
-
A.
Marisa del Toro
chosen
Marisa del Toro is one of the children of acclaimed Mexican filmmaker Guillermo del Toro.
-
B.
Úrsula Corberó
Úrsula Corberó is a Spanish actress best known internationally for her role as Tokyo in the hit television series "Money Heist."
-
C.
Maggie Siff
Maggie Siff is an American actress best known for her television roles in series such as Mad Men, Sons of Anarchy, and Billions.
-
D.
Carmen Ejogo
Carmen Ejogo is a British actress and singer known for her versatile film and television roles, including her acclaimed portrayal of Coretta Scott King in the historical drama "Selma."
-
E.
Karen Rodriguez
Karen Rodriguez is an actress known for her role in the television series "Swarm."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63ecda0c819091f81942a5bde31d |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0e058948190939e936af8f0e221 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 4, 2026, 7:31 p.m.