Triple
T14174811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ba |
E351303
|
entity |
| Predicate | hasNearbyEconomicFeature |
P71206
|
FINISHED |
| Object | sugar mill |
—
|
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: sugar mill | Statement: [Ba, hasNearbyEconomicFeature, sugar mill]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyEconomicFeature Context triple: [Ba, hasNearbyEconomicFeature, sugar mill]
-
A.
hasNearbyEconomicRegion
Indicates that one economic region is geographically close to or adjacent to another economic region.
-
B.
nearbyEconomicActivity
chosen
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
-
C.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
D.
hasMajorCompanyNearby
Indicates that a location or entity is situated close to at least one large or significant company.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b7cc3081909f4fa371e1eae130 |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05baed64819096590e5618a3a8ed |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:02 a.m.