Triple
T26952300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puuc hills |
E678806
|
entity |
| Predicate | hasAgriculturalCondition |
P125911
|
FINISHED |
| Object | thin soils |
—
|
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: thin soils | Statement: [Puuc hills, hasAgriculturalCondition, thin soils]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAgriculturalCondition Context triple: [Puuc hills, hasAgriculturalCondition, thin soils]
-
A.
hasAgriculturalCharacter
Indicates that something possesses qualities, features, or uses typical of agriculture or farming activities.
-
B.
hasAgriculturalChallenge
chosen
Indicates that an entity is experiencing or associated with a difficulty, problem, or obstacle related to agriculture or farming activities.
-
C.
hasAgriculturalType
Indicates that an entity is associated with or classified by a specific type or category of agriculture.
-
D.
hasAgriculturalProduction
Indicates that an entity engages in or is characterized by the production of agricultural goods such as crops or livestock.
-
E.
hasAgriculturalModel
Indicates that one entity possesses, uses, or is associated with a specific agricultural model (such as a framework, simulation, or methodology for agricultural processes).
- 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_69eeeb4e75f08190b14fc91ca4a91488 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6208ac04c8190b42340e5be9d5b52 |
completed | May 2, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 6:25 a.m.