Triple

T2620613
Position Surface form Disambiguated ID Type / Status
Subject Norman Mailer E58998 entity
Predicate givenName P17 FINISHED
Object Norman E1119 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: [Norman Mailer, givenName, Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norman
Context triple: [Norman Mailer, givenName, Norman]
  • A. Norman
    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 chosen
    Norman is a masculine given name of English origin that became widely used in the English-speaking world.
  • 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_69ab4ac558388190962492cd2e1b0ce6 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd897acb481909a976b70304cc30e completed March 7, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69af908ec0cc8190ab8feb8f237eac4b completed March 10, 2026, 3:31 a.m.
Created at: March 6, 2026, 9:50 p.m.