Triple
T3796390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daxing District |
E89779
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Huangcun |
E391596
|
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: Huangcun | Statement: [Daxing District, seat, Huangcun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huangcun Context triple: [Daxing District, seat, Huangcun]
-
A.
Huangcun
chosen
Huangcun is a town in Beijing, China, that serves as the administrative and commercial center of the city's southern Daxing District.
-
B.
Ruchang
Ruchang is a Chinese given name most notably borne by Ding Ruchang, a late Qing dynasty naval commander.
-
C.
Longqing
Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
-
D.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
E.
Hucheng
Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
- 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_69aed9597d6881909b6ee3b9de859223 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee79f09bc8190b7514a11a030eba5 |
completed | March 9, 2026, 3:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b503eef20c8190ad7906275b4b0e67 |
completed | March 14, 2026, 6:45 a.m. |
Created at: March 9, 2026, 3:15 p.m.