Triple
T1120433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuhan Metro |
E11197
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Hanyang |
E18224
|
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: Hanyang | Statement: [Wuhan Metro, serves, Hanyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanyang Context triple: [Wuhan Metro, serves, Hanyang]
-
A.
Hanyang
chosen
Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
-
B.
Anyang
Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
-
C.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
-
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.
Ma’anshan
Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
- 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_69a493252a648190ac48f8742474a5e8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bbbe58588190a5ef6346e269d5f3 |
completed | March 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6f0fac0c8190b23a976b495d1701 |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:43 p.m.