Triple
T33944340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ChiNext board |
E870249
|
entity |
| Predicate | hasSectorConcentration |
P116570
|
FINISHED |
| Object | information technology |
—
|
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: information technology | Statement: [ChiNext board, hasSectorConcentration, information technology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectorConcentration Context triple: [ChiNext board, hasSectorConcentration, information technology]
-
A.
focusSectorCount
Indicates the number of sectors that are designated as a primary or special focus within a given context.
-
B.
portfolioConcentration
Indicates the degree to which investments or holdings are focused in a limited number of assets, sectors, or strategies within a portfolio.
-
C.
hasSectorExposureMethod
Indicates the method or approach by which an entity gains or measures its exposure to a particular sector.
-
D.
hasMarketSector
chosen
Indicates that an entity operates within, is associated with, or belongs to a particular market sector or industry segment.
-
E.
hasRetailConcentration
Indicates that an entity’s activities, assets, or revenues are significantly focused within the retail sector.
- 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_69f3499b0dd48190b07b4b60babcee02 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff965f9be48190b015b788207be676 |
completed | May 9, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69ff95d3015c8190b9d293fe31b859c3 |
completed | May 9, 2026, 8:15 p.m. |
Created at: May 1, 2026, 1:49 a.m.