Triple
T2642174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HKN |
E62893
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hankou Railway Station |
E10464
|
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 Railway Station | Statement: [HKN, locatedIn, Hankou Railway Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hankou Railway Station Context triple: [HKN, locatedIn, Hankou Railway Station]
-
A.
Hankou Railway Station
chosen
Hankou Railway Station is one of the main passenger rail hubs in Wuhan, China, serving as a key node for regional and high-speed train services.
-
B.
Wuchang Railway Station
Wuchang Railway Station is one of the main passenger rail hubs in Wuhan, China, serving as a key node for regional and long-distance train services.
-
C.
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.
-
D.
Dongshankou Station
Dongshankou Station is an underground interchange station on the Guangzhou Metro system serving the Dongshan area of Guangzhou, China.
-
E.
Shanghai Railway Station
Shanghai Railway Station is one of Shanghai’s major railway hubs, serving as a key terminal for high-speed and conventional trains connecting the city with destinations across China.
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd8fdc0bc8190b7fd102b87ee50d1 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa04f0d448190adf113831fb5bc42 |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:53 p.m.