Triple
T1588300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paihuano |
E34116
|
entity |
| Predicate | hasMunicipalSeat |
P1474
|
FINISHED |
| Object | Paihuano |
E34116
|
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: Paihuano | Statement: [Paihuano, hasMunicipalSeat, Paihuano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paihuano Context triple: [Paihuano, hasMunicipalSeat, Paihuano]
-
A.
Paihuano
chosen
Paihuano is a small town and commune in Chile’s Elqui Valley, known for its clear skies, pisco production, and astrotourism.
-
B.
Guanggu
Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
-
C.
Guguan
Guguan is an uninhabited volcanic island in the Northern Mariana Islands chain in the western Pacific Ocean.
-
D.
Nantou
Nantou is a historic subdistrict in Shenzhen’s Nanshan District, known as the site of the old county seat and a preserved ancient town area.
-
E.
Shiyan
Shiyan is an industrial city in northwestern Hubei, China, best known as a center of automobile manufacturing and as a gateway to the nearby Wudang Mountains.
- 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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9090c8e488190aa25893c5ef0fe22 |
completed | March 5, 2026, 4:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad469eaf588190ac6db1c8fb7fa04e |
completed | March 8, 2026, 9:51 a.m. |
Created at: March 4, 2026, 7:27 p.m.