Triple
T32920181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 湖北省部分地区 |
E842122
|
entity |
| Predicate | 主要分布于 |
P2178
|
FINISHED |
| Object | 与湖南接壤的毗邻地带 |
—
|
LITERAL 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: 与湖南接壤的毗邻地带 | Statement: [湖北省部分地区, 主要分布于, 与湖南接壤的毗邻地带]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 主要分布于 Context triple: [湖北省部分地区, 主要分布于, 与湖南接壤的毗邻地带]
-
A.
geographicDistribution
chosen
Indicates the spatial range or area over which something occurs, exists, or is found.
-
B.
ownedAndDistributedInManyRegionsBy
Indicates that something is possessed and actively supplied or made available across multiple geographic areas by a particular entity.
-
C.
geographicDistributionComparedTo
Indicates how the geographic distribution of one entity compares to that of another, such as being broader, narrower, overlapping, or distinct in spatial extent.
-
D.
所在地種別
Indicates the type or category of a location where something is situated or based.
-
E.
countryOrRegionOfPrevalence
Indicates the country or geographic region where something (such as a condition, practice, or phenomenon) is most commonly found or occurs most frequently.
- F. None of above.
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_69f3494779388190a5d3e97f92278be2 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d0d604e88190ad06268f76137168 |
completed | May 3, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:19 a.m.