Triple
T7614674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sal (Cape Verde) |
E172331
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Santa Maria |
E424202
|
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: Santa Maria | Statement: [Sal (Cape Verde), hasSettlement, Santa Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Maria Context triple: [Sal (Cape Verde), hasSettlement, 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
chosen
Santa Maria is a coastal town on the southern tip of Sal Island in Cape Verde, known for its sandy beaches, tourism, and water sports.
-
E.
Santa Maria
Santa Maria is an administrative region within Brazil's Federal District, functioning as part of the greater Brasília metropolitan area.
- 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_69c6994f50808190ba228764bb422417 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fa4392e881908ed1ab3f64b41600 |
completed | March 27, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c87097d3a48190b337ff6906847d4e |
completed | March 29, 2026, 12:21 a.m. |
Created at: March 27, 2026, 3:55 p.m.