Triple

T14467434
Position Surface form Disambiguated ID Type / Status
Subject John Mulaney E358749 entity
Predicate familyName P18 FINISHED
Object Mulaney E702817 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: Mulaney | Statement: [John Mulaney, familyName, Mulaney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mulaney
Context triple: [John Mulaney, familyName, Mulaney]
  • A. Mulaney chosen
    Mulaney is a short-lived semi-autobiographical sitcom created by and starring comedian John Mulaney.
  • B. Maretha
    Maretha is a young girl in the television film adaptation of August Wilson's "The Piano Lesson," representing the family's next generation and their hopes for a better future.
  • C. Tinée
    Tinée is a river in southeastern France that flows through the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
  • D. Lány
    Lány is a village and chateau area in the Czech Republic known as the site of the presidential summer residence and the place where the first Czechoslovak president Tomáš Garrigue Masaryk died.
  • E. Maiya
    Maiya is a reverential term used in parts of India to address or invoke a mother goddess figure, particularly in Hindu devotional contexts.
  • 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_69d827966698819082e140837737501d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91f8613c819080424104c0b7f4c3 completed April 14, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6499c7188190a79411b471cfa7d4 completed May 8, 2026, 4:20 a.m.
Created at: April 10, 2026, 1:19 a.m.