Triple
T13916117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 5 (Beijing Subway) |
E334623
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Huixinxijie Nankou station |
E444952
|
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: Huixinxijie Nankou station | Statement: [Line 5 (Beijing Subway), hasStation, Huixinxijie Nankou station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huixinxijie Nankou station Context triple: [Line 5 (Beijing Subway), hasStation, Huixinxijie Nankou station]
-
A.
Huixinxijie Nankou station
chosen
Huixinxijie Nankou station is a subway station in Beijing that serves passengers on the city's Line 5.
-
B.
Ximenkou station
Ximenkou station is an underground metro station on the Guangzhou Metro system, located near the historic Ximenkou area in central Guangzhou, China.
-
C.
Beixinjing Station
Beixinjing Station is a Shanghai Metro station serving the Beixinjing area in the city's Changning District.
-
D.
Xinzhuang Station
Xinzhuang Station is a major Shanghai Metro interchange station in Minhang District, serving as a key southern transport hub in the city’s network.
-
E.
Luoxi station
Luoxi station is a metro station in Guangzhou, China, serving passengers on the city's rapid transit network.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de27260ae08190be45b4b15898e365 |
completed | April 14, 2026, 11:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d3551cc8190848c14c15da07dc4 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 9, 2026, 10:16 p.m.