Triple

T11287042
Position Surface form Disambiguated ID Type / Status
Subject River Beult E267219 entity
Predicate landUseInCatchment P25275 FINISHED
Object rural farmland 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: rural farmland | Statement: [River Beult, landUseInCatchment, rural farmland]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: landUseInCatchment
Context triple: [River Beult, landUseInCatchment, rural farmland]
  • A. majorLandUse
    Indicates the primary way a given area of land is utilized or designated (e.g., residential, commercial, agricultural).
  • B. landUseIncludes
    Indicates that a specified land area contains or permits the specified type(s) of land use within its boundaries.
  • C. landUseContext
    Indicates the contextual setting or circumstances under which a particular piece of land is used or designated for a specific purpose.
  • D. hasWatershedUse chosen
    Indicates that a particular type of use, activity, or function is associated with or applied to a watershed.
  • E. otherLandUse
    Indicates that the land is used for purposes that do not fall into any of the primary or predefined land-use categories.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e986b0f08190a414749eaa7f1a5d completed April 9, 2026, 6:01 p.m.
PD Predicate disambiguation batch_69d787a240588190aa097298f951c915 completed April 9, 2026, 11:04 a.m.
Created at: April 8, 2026, 9:32 p.m.