Triple
T8664473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elena Anaya |
E205630
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Elena Anaya Gutiérrez |
E205630
|
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: Elena Anaya Gutiérrez | Statement: [Elena Anaya, birthName, Elena Anaya Gutiérrez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elena Anaya Gutiérrez Context triple: [Elena Anaya, birthName, Elena Anaya Gutiérrez]
-
A.
Elena Anaya
chosen
Elena Anaya is a Spanish actress known for her roles in both European cinema and Hollywood productions, including prominent performances in films like "The Skin I Live In."
-
B.
Sofía García
Sofía García is a notable individual distinguished enough to be specifically recognized as a prominent bearer of the García surname.
-
C.
Lorena Bernal
Lorena Bernal is an Argentine-born Spanish actress, model, and former Miss Spain who has also worked as a television presenter.
-
D.
Ana de Armas
Ana de Armas is a Cuban-Spanish actress known for her breakout roles in films such as "Blade Runner 2049," "Knives Out," and "Blonde."
-
E.
Elsa Pataky
Elsa Pataky is a Spanish actress and model best known for her roles in the Fast & Furious film franchise and various international action and thriller movies.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a0ae108190b33dadcc3cb18949 |
completed | March 31, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef37efbf08190805fbb270a4bce37 |
completed | April 2, 2026, 10:53 p.m. |
Created at: March 30, 2026, 6:30 p.m.