Triple

T11772350
Position Surface form Disambiguated ID Type / Status
Subject University of Oxford central area E279929 entity
Predicate hasPart P35 FINISHED
Object Parks Road E377104 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: Parks Road | Statement: [University of Oxford central area, hasPart, Parks Road]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Parks Road
Context triple: [University of Oxford central area, hasPart, Parks Road]
  • A. Parks Road chosen
    Parks Road is a major street in central Oxford, England, running through the university area and lined with prominent academic buildings and museums.
  • B. Brimley Road
    Brimley Road is a major north–south arterial street in Toronto, Ontario, running through neighborhoods such as Agincourt in the city's east end.
  • C. Kelsey Road
    Kelsey Road is a local roadway serving as a primary access route to and through the village of Port Barrington, Illinois.
  • D. Naylor Road
    Naylor Road is a street in the Washington, D.C. area that lends its name to the nearby Naylor Road Metro station on the Green Line.
  • E. Patterson Road
    Patterson Road is a significant thoroughfare running through the Oakwood area of Montgomery County, Ohio, serving as a key local connector for residential and commercial traffic.
  • 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_69d6ab01d2688190ad8ed6bda487eaa5 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a55dfa088190a59b35d0247225e3 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00077201c08190a0c3bb259856d5c9 completed May 10, 2026, 4:20 a.m.
Created at: April 8, 2026, 9:41 p.m.