Triple
T36651275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeungpyeong-gun |
E904859
|
entity |
| Predicate | hasAgricultureAsMainIndustry |
P40002
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Jeungpyeong-gun, hasAgricultureAsMainIndustry, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgricultureAsMainIndustry Context triple: [Jeungpyeong-gun, hasAgricultureAsMainIndustry, true]
-
A.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
B.
hasRuralEconomySector
Indicates that an entity participates in, contains, or is associated with an economic sector based on rural activities or rural development.
-
C.
hasAgriculturalLaborForce
Indicates that an entity possesses a workforce engaged in agricultural activities or farming-related labor.
-
D.
agricultureShareOfGDP
Indicates the proportion of a country’s total economic output (GDP) that is generated by agricultural activities.
-
E.
hasAgriculturalCharacter
chosen
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
- 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_69f76e6d3a3c81909db73eda9e0516bd |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.