Triple
T31421895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changyuan County |
E801551
|
entity |
| Predicate | hasSectorStrength |
P26220
|
FINISHED |
| Object | industrial manufacturing base |
—
|
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: industrial manufacturing base | Statement: [Changyuan County, hasSectorStrength, industrial manufacturing base]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectorStrength Context triple: [Changyuan County, hasSectorStrength, industrial manufacturing base]
-
A.
sectorStrength
chosen
Indicates the relative performance or influence level of a specific sector compared to others within a broader system or market.
-
B.
hasSectorType
Indicates that an entity belongs to or is classified under a particular sector category or type.
-
C.
hasSectorization
Indicates that one entity is divided into, assigned to, or associated with specific sectors defined by another entity.
-
D.
isSectorSpecific
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
E.
hasMarketSector
Indicates that an entity operates within, is associated with, or belongs to a particular market sector or industry segment.
- 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_69f348c26f048190b4adadd71b4596c5 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff069ec1348190815375c5c9e38404 |
completed | May 9, 2026, 10:04 a.m. |
| PD | Predicate disambiguation | batch_69ff05ba57f88190a45d20f18044e0fb |
completed | May 9, 2026, 10 a.m. |
Created at: April 30, 2026, 8:49 p.m.