Triple
T7720916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loudi |
E175006
|
entity |
| Predicate | hasAdministrativeDivision |
P747
|
FINISHED |
| Object | Louxing District |
E691855
|
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: Louxing District | Statement: [Loudi, hasAdministrativeDivision, Louxing District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louxing District Context triple: [Loudi, hasAdministrativeDivision, Louxing District]
-
A.
Louxing District
chosen
Louxing District is the central urban district and administrative seat of Loudi City in Hunan Province, China.
-
B.
Yuhua District
Yuhua District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
-
C.
Xiangfang District
Xiangfang District is an urban district of Harbin in Heilongjiang Province, China, known for its industrial base and role in the city's economic development.
-
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.
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.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702f0366c8190a78f0b03f090fc2c |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9c2e6b2e88190be579a15109d4f82 |
completed | March 30, 2026, 12:25 a.m. |
Created at: March 27, 2026, 4:05 p.m.