Triple
T16513621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lu’an Municipal People’s Government |
E401124
|
entity |
| Predicate | jurisdiction |
P82
|
FINISHED |
| Object | Lu’an City |
E294940
|
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: Lu’an City | Statement: [Lu’an Municipal People’s Government, jurisdiction, Lu’an City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lu’an City Context triple: [Lu’an Municipal People’s Government, jurisdiction, Lu’an City]
-
A.
Yao City
Yao City is a municipality in Osaka Prefecture, Japan, known as a residential and industrial suburb within the Osaka metropolitan area.
-
B.
Sanhe City
Sanhe City is a county-level city in Hebei Province, China, located near Beijing and forming part of the Beijing–Tianjin–Hebei metropolitan region.
-
C.
Wu’an
Wu’an is a county-level city administered by Handan in Hebei Province, northern China, known for its industrial development and coal resources.
-
D.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
E.
Lu'an
chosen
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
- 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e78d4848190a55de9902115b1b2 |
completed | April 18, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0060827d988190b2c0c075a49dfde8 |
completed | May 10, 2026, 10:40 a.m. |
Created at: April 10, 2026, 5:14 a.m.