Triple
T22787243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmen Laffón |
E564002
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Carmen Laffón |
—
|
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: Carmen Laffón | Statement: [Carmen Laffón, name, Carmen Laffón]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carmen Laffón Context triple: [Carmen Laffón, name, Carmen Laffón]
-
A.
Carmen Laffón
chosen
Carmen Laffón was a renowned Spanish painter and sculptor known for her poetic, introspective landscapes and intimate figurative works.
-
B.
Carmen Rabassa
Carmen Rabassa is known primarily as the wife of acclaimed American literary translator Gregory Rabassa.
-
C.
Carmen Cervera
Carmen Cervera is a Spanish socialite, art collector, and former Miss Spain best known for her stewardship of the Thyssen-Bornemisza art collection and her prominent role in European high society.
-
D.
Carmen Calvo
Carmen Calvo is a Spanish conceptual artist known for her evocative mixed-media works that explore memory, identity, and the passage of time.
-
E.
María Belón
María Belón is a Spanish physician and survivor of the 2004 Indian Ocean tsunami whose real-life experience inspired the film "The Impossible."
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c32de6481909ef358d16de98496 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.