Triple

T4294053
Position Surface form Disambiguated ID Type / Status
Subject Count of Calabria E99664 entity
Predicate hasCulture P1439 FINISHED
Object Norman E85853 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: [Count of Calabria, hasCulture, Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norman
Context triple: [Count of Calabria, hasCulture, Norman]
  • A. Norman
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • B. Norman
    Norman is a city in central Oklahoma known for its strong ties to meteorology and atmospheric research, including hosting major national weather institutions.
  • C. Norman chosen
    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.
  • D. Old Norman
    Old Norman is a medieval Romance language that developed in Normandy from Latin and significantly influenced the vocabulary of English and other regional languages.
  • E. Farguson
    Farguson is an alternative spelling of the surname Ferguson, which is of Scottish origin.
  • 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35083f87c8190a3d3b323e76ab575 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c73d47448190a844bc13eae84a54 completed March 14, 2026, 8:38 p.m.
Created at: March 12, 2026, 11:08 p.m.