Triple
T3647408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kandos |
E77333
|
entity |
| Predicate | localEconomyTransition |
P3805
|
FINISHED |
| Object | from heavy industry to tourism and services |
—
|
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: from heavy industry to tourism and services | Statement: [Kandos, localEconomyTransition, from heavy industry to tourism and services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: localEconomyTransition Context triple: [Kandos, localEconomyTransition, from heavy industry to tourism and services]
-
A.
localEconomyImpact
Indicates the effect that an action, event, or entity has on the economic conditions, activities, or performance of a specific local area or community.
-
B.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
C.
economicTransition
chosen
Indicates a change in an entity’s economic system, structure, or status from one state or model to another.
-
D.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
E.
urbanDevelopment
Indicates the process or activities through which urban areas are planned, expanded, or transformed, including changes to infrastructure, land use, and the built environment.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc38aa2388190bf1af926375e2433 |
completed | March 8, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69adb8445b2c8190ab07f6ad4e010d0e |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.