Triple
T22966044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziyang |
E571048
|
entity |
| Predicate | neighboringRegion |
P17964
|
FINISHED |
| Object |
Suining
Suining is a prefecture-level city in central Sichuan Province, China, known as a regional transportation hub with a mix of urban development and surrounding agricultural areas.
|
E1570458
|
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: Suining | Statement: [Ziyang, neighboringRegion, Suining]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suining Context triple: [Ziyang, neighboringRegion, Suining]
-
A.
Suining Xiang
Suining Xiang is a regional variety of the Xiang group of Chinese dialects spoken in and around Suining in Hunan Province, China.
-
B.
Xianning
Xianning is a prefecture-level city in southeastern Hubei Province, China, known for its hot springs, karst landscapes, and historical sites.
-
C.
Quidong
Quidong is a rural locality in New South Wales, Australia, situated within the Snowy Monaro region.
-
D.
Zunhua City
Zunhua City is a county-level city in northeastern Hebei Province, China, known for its historical sites and administrative affiliation with the prefecture-level city of Tangshan.
-
E.
Lu'an
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
- 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: Suining Triple: [Ziyang, neighboringRegion, Suining]
Generated description
Suining is a prefecture-level city in central Sichuan Province, China, known as a regional transportation hub with a mix of urban development and surrounding agricultural areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Suining Target entity description: Suining is a prefecture-level city in central Sichuan Province, China, known as a regional transportation hub with a mix of urban development and surrounding agricultural areas.
-
A.
Suining Xiang
Suining Xiang is a regional variety of the Xiang group of Chinese dialects spoken in and around Suining in Hunan Province, China.
-
B.
Xianning
Xianning is a prefecture-level city in southeastern Hubei Province, China, known for its hot springs, karst landscapes, and historical sites.
-
C.
Quidong
Quidong is a rural locality in New South Wales, Australia, situated within the Snowy Monaro region.
-
D.
Zunhua City
Zunhua City is a county-level city in northeastern Hebei Province, China, known for its historical sites and administrative affiliation with the prefecture-level city of Tangshan.
-
E.
Lu'an
Lu'an is a prefecture-level city in western Anhui Province, China, known for its mountainous terrain and tea production.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1822e542c8190a865f18e64fc0768 |
completed | April 29, 2026, 3:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c1599f45c8190818a8514439aba9d |
completed | May 19, 2026, 7:47 a.m. |
| NEDg | Description generation | batch_6a0c1b212f088190a31e0d314f6424df |
completed | May 19, 2026, 8:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c1b7c4ae0819086f2909140553d4b |
completed | May 19, 2026, 8:12 a.m. |
Created at: April 17, 2026, 3:47 p.m.