Triple

T15462297
Position Surface form Disambiguated ID Type / Status
Subject Dorothy Bartish Shula E371932 entity
Predicate familyName P18 FINISHED
Object Shula E371930 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: Shula | Statement: [Dorothy Bartish Shula, familyName, Shula]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shula
Context triple: [Dorothy Bartish Shula, familyName, Shula]
  • A. Shula chosen
    Shula is a surname most famously associated with Don Shula, the legendary NFL coach of the Miami Dolphins.
  • B. Syal
    Syal is the surname of Meera Syal, a prominent British-Indian comedian, writer, playwright, and actress known for her work in television, film, and literature.
  • C. Ta’aisha
    The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
  • D. Marlo
    Marlo is a fictional character associated with Tully, likely appearing in a narrative centered on that figure.
  • E. Marlo
    Marlo is a small coastal town in East Gippsland, Victoria, Australia, known for its location near the mouth of the Snowy River and its fishing and outdoor recreation.
  • 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_69d85cc8bd308190886949510b42e764 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f1927708190a0d2b63e75469a0e completed April 16, 2026, 1:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff365504488190a6f43d82d008c855 completed May 9, 2026, 1:27 p.m.
Created at: April 10, 2026, 3:32 a.m.