Triple
T14655572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volver |
E344098
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Lola Dueñas |
E866787
|
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: Lola Dueñas | Statement: [Volver, starring, Lola Dueñas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lola Dueñas Context triple: [Volver, starring, Lola Dueñas]
-
A.
Lola Dueñas
chosen
Lola Dueñas is a Spanish actress known for her acclaimed performances in films such as "The Sea Inside" and several collaborations with director Pedro Almodóvar.
-
B.
Silvia Navarro
Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
-
C.
Lola Valente
Lola Valente is a fictional character best known as the ambitious and talented protagonist of the Mexican teen telenovela "Lola, érase una vez."
-
D.
Belén Atienza
Belén Atienza is a Spanish film producer known for her work on acclaimed international films such as "The Impossible" and collaborations with director J.A. Bayona.
-
E.
Verónica Loza
Verónica Loza is a Uruguayan visual artist and performer best known for her multimedia and live visual work with the electronic tango collective Bajofondo.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5de0b98819094c32765e4cb3f9c |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 10, 2026, 1:27 a.m.