Triple
T29378751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gomba District |
E745078
|
entity |
| Predicate | hasTradingCenters |
P142047
|
FINISHED |
| Object | small trading centers scattered across the district |
—
|
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: small trading centers scattered across the district | Statement: [Gomba District, hasTradingCenters, small trading centers scattered across the district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTradingCenters Context triple: [Gomba District, hasTradingCenters, small trading centers scattered across the district]
-
A.
hasRetailCenters
chosen
Indicates that an entity possesses, operates, or is associated with one or more retail centers.
-
B.
hasDistributionCenters
Indicates that one entity maintains or operates one or more distribution centers associated with another entity or area.
-
C.
hasNumberOfCentres
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given entity.
-
D.
isTradingCenter
Indicates that a place functions as a central hub where goods, services, or financial instruments are actively exchanged or traded.
-
E.
hasCommercialCenterType
Indicates that an entity has or is associated with a specific type or category of commercial center (e.g., mall, shopping district, business park).
- 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_69f0a79cfd5481909b4dde750cb8d2c6 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69fddac4e2f48190a9301d3422658b29 |
completed | May 8, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69fdda06969c8190b5d033964ea2a690 |
completed | May 8, 2026, 12:41 p.m. |
Created at: April 28, 2026, 2:33 p.m.