Triple
T12066670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yolanda Andrade |
E287313
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Yolanda Andrade |
E287313
|
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: Yolanda Andrade | Statement: [Yolanda Andrade, name, Yolanda Andrade]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yolanda Andrade Context triple: [Yolanda Andrade, name, Yolanda Andrade]
-
A.
Yolanda Andrade
chosen
Yolanda Andrade is a Mexican actress and television presenter known for her work in telenovelas and talk shows.
-
B.
Mery Andrade
Mery Andrade is a former professional basketball player from Portugal who starred at Old Dominion University before playing in the WNBA and later becoming a coach.
-
C.
Yolanda Ramos
Yolanda Ramos is a Spanish actress and comedian known for her work in television, film, and theater, particularly in Spanish comedy shows and movies.
-
D.
Adriana Medina
Adriana Medina is a contemporary perfumer known for creating modern, luminous fragrances for major luxury brands such as Lancôme.
-
E.
Yolanda Penteado
Yolanda Penteado was a prominent Brazilian arts patron and cultural organizer who played a key role in the development of modern art in São Paulo.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904423dc08190a47194422255c62e |
completed | April 10, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6684b79c48190a663e9f5504ba20c |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:48 p.m.