Triple
T6878479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josef Martínez |
E158731
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Martínez |
E39908
|
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: Martínez | Statement: [Josef Martínez, familyName, Martínez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martínez Context triple: [Josef Martínez, familyName, Martínez]
-
A.
Martínez
chosen
Martínez is a common Spanish-language surname widely borne across Spain and Latin America.
-
B.
Gutiérrez
Gutiérrez is a common Spanish-language surname borne by numerous individuals across the Spanish-speaking world.
-
C.
Vázquez
Vázquez is a Spanish-language surname commonly found in Spain and Latin America, borne by various notable figures in entertainment, sports, and public life.
-
D.
González
González is a common Spanish-language surname widely borne across Spain and Latin America, often associated with Iberian heritage.
-
E.
Magaña
Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
- 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_69c68832af1481908ce356e133ebaebe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8e498bc81908b2fbe0c6a8b95b7 |
completed | March 27, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c742c2b81881909bd13df0d6028cc6 |
completed | March 28, 2026, 2:53 a.m. |
Created at: March 27, 2026, 2:22 p.m.