Triple
T8919338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shikumen |
E212370
|
entity |
| Predicate | notableExample |
P1503
|
FINISHED |
| Object | Tianzifang |
E713422
|
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: Tianzifang | Statement: [Shikumen, notableExample, Tianzifang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tianzifang Context triple: [Shikumen, notableExample, Tianzifang]
-
A.
Tianzifang
chosen
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.
-
B.
Dongsi
Dongsi is a historic neighborhood and street-crossroads area in central Beijing known for its traditional hutong lanes and long-standing commercial streets.
-
C.
Qiaocheng
Qiaocheng is a historical name for the area now known as Bozhou, a city in Anhui Province, China, with a long history and cultural significance.
-
D.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
E.
Zhushikou
Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
- 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6613639881909090d060f388a865 |
completed | April 1, 2026, 12:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1d31f84819098c34c2589949c6e |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:56 p.m.