Triple

T12743468
Position Surface form Disambiguated ID Type / Status
Subject Tyneside urban area E304544 entity
Predicate hasPart P35 FINISHED
Object Felling E304542 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: Felling | Statement: [Tyneside urban area, hasPart, Felling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Felling
Context triple: [Tyneside urban area, hasPart, Felling]
  • A. Felling chosen
    Felling is a town in Tyne and Wear, England, situated on the south bank of the River Tyne near Gateshead and Newcastle upon Tyne.
  • B. The Woods
    The Woods is a residential neighborhood within the planned community of Burke Centre in Fairfax County, Virginia.
  • C. The Woods
    The Woods is a film scored by composer John Frizzell, known for its atmospheric and suspenseful musical style.
  • D. The Wood
    "The Wood" is a 1999 coming-of-age comedy-drama film that follows three lifelong friends in Inglewood, California, as they reminisce about their youth on the day of one friend's wedding.
  • E. The Sawmill
    The Sawmill is a 1922 silent comedy short film starring slapstick comedian Larry Semon, known for its energetic gags and elaborate stunt-filled sequences.
  • 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_69d7bdf1426c8190a4402e1c4cdec33a completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96bd321bc81908eb61cc05b550754 completed April 10, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c91ed7c81909165dc300d5b23ae completed May 2, 2026, 10:37 p.m.
Created at: April 9, 2026, 5:26 p.m.