Triple
T5131463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Óscar Arias Sánchez Esquivel |
E115708
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Esquivel |
E115708
|
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: Esquivel | Statement: [Óscar Arias Sánchez Esquivel, familyName, Esquivel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esquivel Context triple: [Óscar Arias Sánchez Esquivel, familyName, Esquivel]
-
A.
Esquivel
chosen
Esquivel is a Spanish-language surname borne by various notable figures in literature, politics, and the arts across Latin America.
-
B.
Herrera
Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
-
C.
Echeandía
Echeandía is a small town in central Ecuador known for its agricultural activities and rural Andean setting.
-
D.
Echeverría
Echeverría is a Spanish-language surname borne by various notable figures in politics, literature, and the arts across the Spanish-speaking world.
-
E.
Balderas
Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
- 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_69bd444426bc819099ccd23f141e22aa |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd782904788190b544540483915c0e |
completed | March 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beefa31458819094b53cbc7a2f5677 |
completed | March 21, 2026, 7:21 p.m. |
Created at: March 20, 2026, 1:42 p.m.