Triple
T2895107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hankou–Danjiangkou Railway |
E63919
|
entity |
| Predicate | startPoint |
P389
|
FINISHED |
| Object | Hankou |
E1680
|
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: Hankou | Statement: [Hankou–Danjiangkou Railway, startPoint, Hankou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hankou Context triple: [Hankou–Danjiangkou Railway, startPoint, Hankou]
-
A.
Wuhan
chosen
Wuhan is a major city in central China, known as a key industrial, commercial, and transportation hub located at the confluence of the Yangtze and Han rivers.
-
B.
Huangzhou
Huangzhou is the central urban district and administrative heart of Huanggang in Hubei Province, China.
-
C.
Yichang
Yichang is a key city in western Hubei, China, best known as the gateway to the Three Gorges region and the nearby Three Gorges Dam on the Yangtze River.
-
D.
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.
-
E.
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.
- 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_69ab4c45822c8190830c5f2bb97bcfd0 |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe06509808190b673222b9ae3d599 |
completed | March 7, 2026, 8:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a495fbc81909e40627a3feb9f42 |
completed | March 12, 2026, 7:55 p.m. |
Created at: March 6, 2026, 10:08 p.m.