Triple
T2266491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinshan District |
E50156
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Fengxian District |
E50007
|
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: Fengxian District | Statement: [Jinshan District, borders, Fengxian District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fengxian District Context triple: [Jinshan District, borders, Fengxian District]
-
A.
Fengxian District
chosen
Fengxian District is a suburban district in the southern part of Shanghai, China, known for its coastal location, agricultural areas, and growing residential and industrial development.
-
B.
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.
-
C.
Xiaonan District
Xiaonan District is the central urban district and administrative seat of Xiaogan City in Hubei Province, China.
-
D.
Xicheng District
Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
-
E.
Qingshan District
Qingshan District is an urban district of Wuhan in Hubei Province, China, known for its heavy industry and riverside location along the Yangtze River.
- 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_69a88b01e0048190ba96431b5f990ba9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc18fff0c8190acd73d8db8a41cff |
completed | March 7, 2026, 6:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce777e5081909ed7e3e60bf33503 |
completed | March 10, 2026, 7:55 a.m. |
Created at: March 4, 2026, 7:48 p.m.