Triple
T1689175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Felipe de Aconcagua Province |
E36511
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | San Felipe |
E79916
|
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: San Felipe | Statement: [San Felipe de Aconcagua Province, hasCity, San Felipe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Felipe Context triple: [San Felipe de Aconcagua Province, hasCity, San Felipe]
-
A.
San Felipe
chosen
San Felipe is a historic city in central Chile known for its agricultural surroundings and role as a commercial and administrative center in the Aconcagua Valley.
-
B.
San Carlos
San Carlos is a Nicaraguan town that serves as a key river and lake port near the southeastern end of Lake Nicaragua.
-
C.
San Carlos
San Carlos is a Chilean city known as an agricultural and commercial center in the Ñuble Region.
-
D.
San Carlos
San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
-
E.
Tacuba
Tacuba is a historic neighborhood in Mexico City known for its colonial-era architecture and role as a former pre-Hispanic town.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6296655c8190835ec0d20f7460ca |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0cb18348190baf7a30c231c7349 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 4, 2026, 7:29 p.m.