Triple
T7946385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Purple Cloud Temple |
E184507
|
entity |
| Predicate | locatedInAdministrativeTerritory |
P40
|
FINISHED |
| Object | Shiyan |
E36652
|
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: Shiyan | Statement: [Purple Cloud Temple, locatedInAdministrativeTerritory, Shiyan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiyan Context triple: [Purple Cloud Temple, locatedInAdministrativeTerritory, Shiyan]
-
A.
Shiyan
chosen
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.
-
B.
Jinyang
Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi province.
-
C.
Kaili
Kaili is a county-level city in southeastern Guizhou, China, known as a cultural center of the Miao and Dong ethnic minorities and a gateway to surrounding minority villages.
-
D.
Guanggu
Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
-
E.
Sihui
Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
- 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_69ca8291c2008190b1b8832c87814bcf |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b29a570819091a2ac185a8d57c4 |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe02faa308190aeba83cc6cb96153 |
completed | March 31, 2026, 2:54 p.m. |
Created at: March 30, 2026, 5:09 p.m.