Triple
T8199135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butler Township, Adams County, Pennsylvania |
E191520
|
entity |
| Predicate | isAgricultural |
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: [Butler Township, Adams County, Pennsylvania, isAgricultural, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAgricultural Context triple: [Butler Township, Adams County, Pennsylvania, isAgricultural, true]
-
A.
hasAgriculturalCharacter
chosen
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
-
B.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
C.
usedAgriculture
Indicates that an entity employed agricultural methods, practices, or resources for cultivation, production, or related purposes.
-
D.
representsAgriculture
Indicates that one entity serves as an example, instance, or embodiment of agriculture in relation to another entity.
-
E.
agricultureUse
Indicates that something is used for, involved in, or designated for agricultural activities or purposes.
- 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_69ca82c6e9548190a4c5ca14516e4417 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb5df426cc81908b676d3d6852e29f |
completed | March 31, 2026, 5:39 a.m. |
| PD | Predicate disambiguation | batch_69cb36aac86081909b83636e352e0ced |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:42 p.m.