Triple
T15645266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRTS |
E376160
|
entity |
| Predicate | majorInterchangeStation |
P30882
|
FINISHED |
| Object |
Dongmen Station
Dongmen Station is a key Taipei Metro interchange hub connecting multiple subway lines in central Taipei, Taiwan.
|
E1231683
|
NE FINISHED |
How this triple was built (4 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: Dongmen Station | Statement: [TRTS, majorInterchangeStation, Dongmen Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongmen Station Context triple: [TRTS, majorInterchangeStation, Dongmen Station]
-
A.
Chunxi Road Station
Chunxi Road Station is a major metro station in Chengdu, China, providing access to the popular commercial and shopping district around Chunxi Road.
-
B.
Dongsi station
Dongsi station is a Beijing Subway interchange station in central Beijing that serves both Line 5 and Line 6.
-
C.
Nanpu station
Nanpu station is a metro station in Guangzhou, China, serving passengers on the city’s Line 2 rapid transit route.
-
D.
Xintiandi station
Xintiandi station is a major Shanghai Metro interchange located near the popular Xintiandi entertainment and shopping district.
-
E.
Zhongnan Road Station
Zhongnan Road Station is a major interchange stop on the Wuhan Metro, serving as a key transit hub in the city’s central Wuchang District.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dongmen Station Triple: [TRTS, majorInterchangeStation, Dongmen Station]
Generated description
Dongmen Station is a key Taipei Metro interchange hub connecting multiple subway lines in central Taipei, Taiwan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dongmen Station Target entity description: Dongmen Station is a key Taipei Metro interchange hub connecting multiple subway lines in central Taipei, Taiwan.
-
A.
Chunxi Road Station
Chunxi Road Station is a major metro station in Chengdu, China, providing access to the popular commercial and shopping district around Chunxi Road.
-
B.
Dongsi station
Dongsi station is a Beijing Subway interchange station in central Beijing that serves both Line 5 and Line 6.
-
C.
Nanpu station
Nanpu station is a metro station in Guangzhou, China, serving passengers on the city’s Line 2 rapid transit route.
-
D.
Xintiandi station
Xintiandi station is a major Shanghai Metro interchange located near the popular Xintiandi entertainment and shopping district.
-
E.
Zhongnan Road Station
Zhongnan Road Station is a major interchange stop on the Wuhan Metro, serving as a key transit hub in the city’s central Wuchang District.
- F. None of above. chosen
Provenance (5 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_69d85cd1564c8190991adda63bfab4b0 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a502c82881908d5b6f7c23e8a403 |
completed | May 10, 2026, 3:32 p.m. |
| NEDg | Description generation | batch_6a00a5b3ce848190a73f06d9708bfc85 |
completed | May 10, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00a6734d008190bb0a5aa28826e73a |
completed | May 10, 2026, 3:38 p.m. |
Created at: April 10, 2026, 4:15 a.m.