Triple
T12210565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Feng County |
E290944
|
entity |
| Predicate | hasAgriculturalBase |
P25683
|
FINISHED |
| Object | grain production |
—
|
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: grain production | Statement: [Feng County, hasAgriculturalBase, grain production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgriculturalBase Context triple: [Feng County, hasAgriculturalBase, grain production]
-
A.
hasAgriculturalProduction
chosen
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
B.
isAgriculturalCity
Indicates that a city is primarily characterized by agriculture-based activities, economy, or land use.
-
C.
hasAgriculturalCharacter
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
-
D.
hasRuralEconomySector
Indicates that an entity participates in, contains, or is associated with an economic sector based on rural activities or rural development.
-
E.
hasAgriculturalAssociation
Indicates that there exists a formal or recognized connection between an entity and an agricultural organization, group, or activity.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.