Triple

T20205966
Position Surface form Disambiguated ID Type / Status
Subject Alan Kohan E493350 entity
Predicate familyName P18 FINISHED
Object Kohan NE NERFINISHED

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: Kohan | Statement: [Alan Kohan, familyName, Kohan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kohan
Context triple: [Alan Kohan, familyName, Kohan]
  • A. Kohan chosen
    Kohan is a surname most prominently associated with American television writer and producer Jenji Kohan, known for creating the series "Weeds" and "Orange Is the New Black."
  • B. Kusaila
    Kusaila was a 7th-century Berber Christian leader and military commander who led resistance against the early Muslim expansion in North Africa.
  • C. Saarang
    Saarang is the annual cultural festival of IIT Madras, known as one of India’s largest and most prominent college cultural fests featuring music, arts, literary, and performing arts events.
  • D. Koho
    Koho is an Austroasiatic language spoken by the Koho ethnic group in Vietnam’s Central Highlands.
  • E. Nasib
    Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6269614c8190bb40475d9d477358 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66d922ebc8190ae012da8ceba74dd completed April 20, 2026, 6:16 p.m.
Created at: April 11, 2026, 11:38 p.m.