Triple

T15459877
Position Surface form Disambiguated ID Type / Status
Subject Ham, London E371867 entity
Predicate hasGreenSpace P1495 FINISHED
Object Ham Lands E677006 NE 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: Ham Lands | Statement: [Ham, London, hasGreenSpace, Ham Lands]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ham Lands
Context triple: [Ham, London, hasGreenSpace, Ham Lands]
  • A. Ham Lands chosen
    Ham Lands is a large riverside nature reserve and public open space in Ham, southwest London, known for its diverse wildlife habitats and recreational walking areas along the Thames.
  • B. Hageland
    Hageland is a hilly, rural region in the eastern part of Flemish Brabant in Belgium, known for its orchards, vineyards, and scenic landscapes.
  • C. Jerrys Plains
    Jerrys Plains is a small rural village in New South Wales, Australia, known for its agricultural lands, horse studs, and proximity to the Hunter Valley wine region.
  • D. Hunter Hills
    Hunter Hills is a rural hilly area in South Canterbury, New Zealand, known for its pastoral farmland and scenic landscapes near the town of Waimate.
  • E. Vidzeme Upland
    Vidzeme Upland is a hilly highland region in northeastern Latvia known for containing the country’s highest elevations and scenic landscapes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d85cc8bd308190886949510b42e764 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f17663c8190b995c7c3129c90d6 completed April 16, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21b9817c819082e5c571b08bafb5 completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:32 a.m.