Triple
T1834700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiaogan |
E41038
|
entity |
| Predicate | administrativeDivision |
P747
|
FINISHED |
| Object | Xiaonan District |
E263374
|
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: Xiaonan District | Statement: [Xiaogan, administrativeDivision, Xiaonan District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiaonan District Context triple: [Xiaogan, administrativeDivision, Xiaonan District]
-
A.
Xiaonan District
chosen
Xiaonan District is the central urban district and administrative seat of Xiaogan City in Hubei Province, China.
-
B.
Xiaoting District
Xiaoting District is an urban administrative district of Yichang in Hubei Province, China, known for its location along the Yangtze River and its role in the region’s industrial and transportation network.
-
C.
Yuhua District
Yuhua District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
-
D.
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.
-
E.
Xicheng District
Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb026aa7c8190bc988d3ee0fd9f41 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef071a6588190bf45a797b4d10f8b |
completed | March 9, 2026, 4:08 p.m. |
Created at: March 4, 2026, 7:33 p.m.