Triple
T16061316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gu'an County |
E389618
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Gu'an Town |
E1191138
|
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: Gu'an Town | Statement: [Gu'an County, seat, Gu'an Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gu'an Town Context triple: [Gu'an County, seat, Gu'an Town]
-
A.
Gu'an Town
chosen
Gu'an Town is the administrative and economic center of Gu'an County in Hebei Province, China.
-
B.
Chengguan town
Chengguan town is the main urban hub and political, economic, and cultural center of Yuzhong County in Gansu Province, China.
-
C.
Gaojing Town
Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, China.
-
D.
Gaotangling town
Gaotangling town is an urban township that serves as the main commercial and administrative center of Wangcheng County in Hunan Province, China.
-
E.
Xikou Town
Xikou Town is a historic town in Fenghua District, Ningbo, Zhejiang Province, best known as the hometown of Chiang Kai-shek and a popular cultural and tourist destination.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183795100819097be92e6d07dc5b1 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe47a92608190993fe7f2c5957019 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:57 a.m.