Triple
T4369682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hefei Railway Station |
E98864
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Hefei Metro |
E103838
|
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: Hefei Metro | Statement: [Hefei Railway Station, connectsTo, Hefei Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hefei Metro Context triple: [Hefei Railway Station, connectsTo, Hefei Metro]
-
A.
Hefei Metro
chosen
Hefei Metro is the rapid transit system serving Hefei, the capital city of China’s Anhui Province, providing urban rail transportation across the metropolitan area.
-
B.
Hangzhou Metro
Hangzhou Metro is the rapid transit system serving the city of Hangzhou, China, providing urban and suburban rail transportation across the metropolitan area.
-
C.
Nanchang Metro
Nanchang Metro is the rapid transit system serving the city of Nanchang in Jiangxi Province, China.
-
D.
Nanjing Metro
Nanjing Metro is the rapid transit system serving the city of Nanjing, China, comprising multiple urban and suburban lines that form a major part of the city's public transportation network.
-
E.
Wuhan Metro
Wuhan Metro is the rapid transit system serving the city of Wuhan, China, providing urban rail transportation across its major districts.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352052b388190b02cca9a3b480be4 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbd1646c8190b7db5d9a9445633c |
completed | March 14, 2026, 10:06 p.m. |
Created at: March 12, 2026, 11:17 p.m.