Triple
T3549410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qianmen station |
E75073
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Xicheng District |
E66174
|
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: Xicheng District | Statement: [Qianmen station, locatedIn, Xicheng District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xicheng District Context triple: [Qianmen station, locatedIn, Xicheng District]
-
A.
Xicheng District
chosen
Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
-
B.
Xialu District
Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
-
C.
Jianye District
Jianye District is an urban district of Nanjing, China, known for its historical significance and major memorial sites related to the Nanjing Massacre.
-
D.
Tianxin District
Tianxin District is a central urban district of Changsha, the capital city of Hunan Province in China, known for its historical sites and commercial areas.
-
E.
Jinyuan District
Jinyuan District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
- 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_69ad85d33c6c819081d5ac1df13b5680 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbfd278348190ad2fa54f4a423541 |
completed | March 8, 2026, 6:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589b716648190aeaead138203cbf9 |
completed | March 14, 2026, 4:15 p.m. |
Created at: March 8, 2026, 3:20 p.m.