Triple
T16061315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gu'an County |
E389618
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Gu'an Town
Gu'an Town is the administrative and economic center of Gu'an County in Hebei Province, China.
|
E1191138
|
NE FINISHED |
How this triple was built (4 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, capital, 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, capital, Gu'an Town]
-
A.
Chengguan town
Chengguan town is the main urban hub and political, economic, and cultural center of Yuzhong County in Gansu Province, China.
-
B.
Gaojing Town
Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, China.
-
C.
Gaotangling town
Gaotangling town is an urban township that serves as the main commercial and administrative center of Wangcheng County in Hunan Province, China.
-
D.
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.
-
E.
Zhushan Town
Zhushan Town is the main urban and political hub of Zhushan County in Hubei Province, China, serving as its central seat of local government and administration.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gu'an Town Triple: [Gu'an County, capital, Gu'an Town]
Generated description
Gu'an Town is the administrative and economic center of Gu'an County in Hebei Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gu'an Town Target entity description: Gu'an Town is the administrative and economic center of Gu'an County in Hebei Province, China.
-
A.
Chengguan town
Chengguan town is the main urban hub and political, economic, and cultural center of Yuzhong County in Gansu Province, China.
-
B.
Gaojing Town
Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, China.
-
C.
Gaotangling town
Gaotangling town is an urban township that serves as the main commercial and administrative center of Wangcheng County in Hunan Province, China.
-
D.
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.
-
E.
Zhushan Town
Zhushan Town is the main urban and political hub of Zhushan County in Hubei Province, China, serving as its central seat of local government and administration.
- F. None of above. chosen
Provenance (5 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_69ffdbe88a608190bc0a0cbfdb71e81d |
completed | May 10, 2026, 1:14 a.m. |
| NEDg | Description generation | batch_69ffdce9591c81909e6bb5c13ddf84cd |
completed | May 10, 2026, 1:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffddb0ff848190ace70b55d9861040 |
completed | May 10, 2026, 1:21 a.m. |
Created at: April 10, 2026, 4:57 a.m.