Triple
T7613983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colchagua Valley |
E172314
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | Apalta |
E563654
|
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: Apalta | Statement: [Colchagua Valley, hasSubregion, Apalta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apalta Context triple: [Colchagua Valley, hasSubregion, Apalta]
-
A.
Topete
Topete is a Spanish surname associated with notable figures such as admiral Pascual Cervera y Topete.
-
B.
Tezonco
Tezonco is a metro station in Mexico City that serves the southeastern area of the city on the capital’s rapid transit network.
-
C.
Apalta Valley
chosen
Apalta Valley is a renowned wine-growing area in central Chile’s Colchagua region, celebrated for its premium red wines, especially Carmenère and Syrah.
-
D.
Tafoya
Tafoya is the surname of Michele Tafoya, a prominent American sportscaster best known for her work as an NFL sideline reporter.
-
E.
Metztitlán
Metztitlán is a town and municipality in the state of Hidalgo, Mexico, known for its dramatic canyon landscapes, rich biodiversity, and the nearby Metztitlán Biosphere Reserve.
- 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_69c6fa418ef081908fd17ff367520995 |
completed | March 27, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c93a41dfe081908ca5f7d5d46a7489 |
completed | March 29, 2026, 2:42 p.m. |
Created at: March 27, 2026, 3:55 p.m.