Triple
T17829867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dongsi station |
E445222
|
entity |
| Predicate | servesArea |
P82
|
FINISHED |
| Object | Dongsi |
—
|
NE NERFINISHED |
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: Dongsi | Statement: [Dongsi station, servesArea, Dongsi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongsi Context triple: [Dongsi station, servesArea, Dongsi]
-
A.
Dongsi
chosen
Dongsi is a historic neighborhood and street-crossroads area in central Beijing known for its traditional hutong lanes and long-standing commercial streets.
-
B.
Dongzhimen
Dongzhimen is a major commercial and transportation hub in Beijing, known for its historic city gate site, busy subway interchange, and airport express connection.
-
C.
Dongdan
Dongdan is a central commercial and transportation hub in Beijing known for its shopping streets, offices, and busy intersections.
-
D.
Dongmen
Dongmen is a key Taipei Metro station in central Taipei that serves as a busy transfer point between multiple subway lines and nearby commercial and residential areas.
-
E.
Tianzifang
Tianzifang is a popular arts and crafts enclave in Shanghai known for its narrow alleyways, renovated traditional shikumen buildings, and vibrant mix of boutiques, galleries, cafés, and bars.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48917c4d88190b919a4b75aed011c |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 10, 2026, 10:15 a.m.