Triple
T13851072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jincheng |
E332941
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Chengqu |
E1065873
|
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: Chengqu | Statement: [Jincheng, capital, Chengqu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chengqu Context triple: [Jincheng, capital, Chengqu]
-
A.
Chengqu
chosen
Chengqu is the central urban district and administrative hub of Jincheng in Shanxi Province, China.
-
B.
Changle
Changle is a coastal city in eastern China located on the Shandong Peninsula.
-
C.
Chéngdū
Chéngdū is the capital of China’s Sichuan province, known for its spicy cuisine, giant panda breeding centers, and long history as a major cultural and economic hub in western China.
-
D.
Changge City
Changge City is a county-level city in central China's Henan Province, administered by the prefecture-level city of Xuchang and known for its manufacturing and agricultural industries.
-
E.
Chengguan
Chengguan was an influential Tang dynasty Buddhist monk and scholar renowned for his authoritative commentaries on Huayan (Avatamsaka) doctrine.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02d8fb788190baef7537be2baecb |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c70a59e8819090b750699993a107 |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:14 p.m.