Triple
T7740167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valdivia Province |
E175485
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mariquina |
E112180
|
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: Mariquina | Statement: [Valdivia Province, contains, Mariquina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariquina Context triple: [Valdivia Province, contains, Mariquina]
-
A.
Mariquina
chosen
Mariquina is a commune and town in southern Chile, located in the Los Ríos Region and known for its rural landscapes and Mapuche cultural presence.
-
B.
Pacasmayo
Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
-
C.
Balbuena
Balbuena is a metro station on Mexico City’s Line 1 serving the Balbuena neighborhood in the eastern part of the city.
-
D.
Cáqueza
Cáqueza is a small municipality and town in the Andean region of central Colombia, known for its rural landscapes and proximity to Bogotá in the department of Cundinamarca.
-
E.
Guagua
Guagua is a municipality in the province of Pampanga in the Philippines, known historically as a riverside trading 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_69c6995f9c60819092e386192bd63c6f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7035cddb881908bdfc1bd7d6a64ad |
completed | March 27, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be4178408190850c284aab895442 |
completed | March 29, 2026, 5:53 a.m. |
Created at: March 27, 2026, 4:07 p.m.