Triple
T37861452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuliangye baijiu |
E944341
|
entity |
| Predicate | producerHeadquartersLocation |
P91520
|
FINISHED |
| Object | Yibin, Sichuan, China |
—
|
NE NERFINISHED |
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: Yibin, Sichuan, China | Statement: [Wuliangye baijiu, producerHeadquartersLocation, Yibin, Sichuan, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: producerHeadquartersLocation Context triple: [Wuliangye baijiu, producerHeadquartersLocation, Yibin, Sichuan, China]
-
A.
employerHeadquarters
Indicates the location where an employer’s main corporate offices or central administrative operations are based.
-
B.
productionCompanyLocation
Indicates the relationship between a production company and the geographic location where it is based or operates.
-
C.
companyHeadquartersAtDesignTime
Indicates the location where a company’s headquarters are situated at a specific, defined point in time (typically when the data or model was created).
-
D.
hasManufacturerHeadquartersIn
chosen
Indicates that the location specified is the place where the manufacturer’s main headquarters is situated.
-
E.
headquartersLocation
Indicates the place where an organization’s main administrative center or principal office is located.
- F. None of above.
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_69f76eee2f9c8190b1272aa2ee55ebf5 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff409ff5548190849c2d50e99bd807 |
completed | May 9, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69ff401a5e188190a72f945e910b4a6c |
completed | May 9, 2026, 2:09 p.m. |
Created at: May 3, 2026, 4:19 p.m.