Triple

T17471922
Position Surface form Disambiguated ID Type / Status
Subject Joe Noland E425436 entity
Predicate partOf P40 FINISHED
Object The District (TV series) 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: The District (TV series) | Statement: [Joe Noland, partOf, The District (TV series)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The District (TV series)
Context triple: [Joe Noland, partOf, The District (TV series)]
  • A. The District chosen
    The District is an American television drama series that follows the efforts of a tough police commissioner working to reform and reduce crime in Washington, D.C.
  • B. The Legal District
    The Legal District is a prominent area of Dunwall known for housing the city’s courts, legal institutions, and offices of influential lawyers and officials.
  • C. Better Know a District
    Better Know a District is a recurring satirical segment on The Colbert Report in which Stephen Colbert humorously profiles individual U.S. congressional districts and their representatives.
  • D. The District universe
    The District universe is the fictional setting in which the events and characters associated with Temple Page’s work take place.
  • E. Sur district
    Sur district is the historic central district of Diyarbakır, Turkey, known for its ancient city walls and densely built old city.
  • 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_69d889dbc2e88190b18ea6115e819258 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e451b8a51081908d94bebe2417e3d3 completed April 19, 2026, 3:53 a.m.
Created at: April 10, 2026, 5:47 a.m.