Triple
T22162388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santamaría |
E547702
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Santa Maria |
—
|
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: Santa Maria | Statement: [Santamaría, hasVariant, Santa Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Maria Context triple: [Santamaría, hasVariant, Santa Maria]
-
A.
Santa Maria
Santa Maria is a city in California’s Central Coast region known for its agriculture, wine industry, and distinctive Santa Maria–style barbecue.
-
B.
Santa Maria
Santa Maria is a popular sandy beach on the Greek island of Paros, known for its clear waters, water sports, and lively summer atmosphere.
-
C.
Santa Maria
Santa Maria is a first-class, landlocked municipality in the province of Bulacan in the Philippines, known for its rapidly growing suburban communities and proximity to Metro Manila.
-
D.
Santa Maria
Santa Maria is a major city in southern Brazil known as an educational and military center in the state of Rio Grande do Sul.
-
E.
Santa Maria
Santa Maria is a coastal municipality in the province of Ilocos Sur in the Philippines, known for its historic church and agricultural communities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a2e47a88190a3b5c05398605f68 |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:34 p.m.