Triple
T504200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hankou Railway Station |
E10464
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Wuhan Metro |
E11197
|
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: Wuhan Metro | Statement: [Hankou Railway Station, connectedTo, Wuhan Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wuhan Metro Context triple: [Hankou Railway Station, connectedTo, Wuhan Metro]
-
A.
Wuhan Metro
chosen
Wuhan Metro is the rapid transit system serving the city of Wuhan, China, providing urban rail transportation across its major districts.
-
B.
Guangzhou Metro
Guangzhou Metro is the rapid transit system serving Guangzhou, China, forming one of the country’s largest and busiest urban rail networks.
-
C.
Shanghai Metro
Shanghai Metro is one of the world’s largest and busiest rapid transit systems, serving the city of Shanghai with an extensive network of urban and suburban rail lines.
-
D.
Beijing Subway
The Beijing Subway is one of the world’s largest and busiest rapid transit systems, forming the backbone of public transportation in China’s capital city.
-
E.
Wuhan Railway Station
Wuhan Railway Station is a major modern high-speed rail hub in Wuhan, China, known for its large scale and distinctive, wave-like architectural design.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f149bd1c81908ff58ac504ace2bf |
completed | Feb. 28, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4b5d3b5f0819093c5d55bdf5a7c4d |
completed | March 1, 2026, 9:55 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.