Triple
T21321492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martín de Ursúa y Arizmendi |
E525628
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Martín |
—
|
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: Martín | Statement: [Martín de Ursúa y Arizmendi, givenName, Martín]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martín Context triple: [Martín de Ursúa y Arizmendi, givenName, Martín]
-
A.
Martín
chosen
Martín is a masculine given name of Latin origin, commonly used in Spanish-speaking countries and derived from the name Martinus, associated with the Roman god Mars.
-
B.
Sebastián
Sebastián is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
C.
Sebastián
Sebastián is a renowned Mexican sculptor celebrated for his monumental geometric public artworks.
-
D.
Martín (Hache)
Martín (Hache) is a 1997 Argentine-Spanish drama film that explores generational conflict, identity, and disillusionment through the strained relationship between a troubled young man and his estranged father in Madrid.
-
E.
Alejo
Alejo is a Spanish given name commonly used as a short form of Alejandro.
- 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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e77ed1538c8190954da114e49dfa36 |
completed | April 21, 2026, 1:42 p.m. |
Created at: April 16, 2026, 4:39 p.m.