Triple

T1562018
Position Surface form Disambiguated ID Type / Status
Subject University of Oklahoma E33346 entity
Predicate city P40 FINISHED
Object Norman E41178 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: Norman | Statement: [University of Oklahoma, city, Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norman
Context triple: [University of Oklahoma, city, Norman]
  • A. Norman chosen
    Norman is a city in central Oklahoma known for its strong ties to meteorology and atmospheric research, including hosting major national weather institutions.
  • B. Norman
    The Normans were a medieval people of Viking origin who settled in northern France and became influential conquerors and rulers across Europe and the Mediterranean, notably shaping the culture and politics of regions such as England, southern Italy, and Sicily.
  • C. Norman
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • D. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • E. Southery
    Southery is a village and civil parish in Norfolk, England, situated in the Fens near the River Great Ouse.
  • 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_69a885ef9cf48190b0af0f5ce3d02231 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908886efc8190a630ef1e06d0d9cb completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad37151ad48190a8c25899b488c68b completed March 8, 2026, 8:45 a.m.
Created at: March 4, 2026, 7:27 p.m.