Triple
T20599672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhu Xi |
E506139
|
entity |
| Predicate | artName |
P32318
|
FINISHED |
| Object | Ziyang |
—
|
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: Ziyang | Statement: [Zhu Xi, artName, Ziyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziyang Context triple: [Zhu Xi, artName, Ziyang]
-
A.
Ziyang
Ziyang is a city in Sichuan Province, China, located within the fertile and densely populated Chengdu Plain region.
-
B.
Ziyang
chosen
Ziyang is the art name (courtesy name) of Zhu Xi, the influential Neo-Confucian philosopher of the Southern Song dynasty.
-
C.
Xiangyang
Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
-
D.
Ezhou
Ezhou is a prefecture-level city in eastern Hubei Province, China, known for its location along the Yangtze River and its growing role as a regional transportation and industrial hub.
-
E.
Suizhou
Suizhou is a county-level city in northern Hubei Province, China, known for its historical sites and role as a regional transport and economic hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa1ef9ac8190b05e23c149529cb9 |
completed | April 20, 2026, 10:35 p.m. |
Created at: April 16, 2026, 11:40 a.m.