Triple

T20820220
Position Surface form Disambiguated ID Type / Status
Subject Frank Bledsoe E512554 entity
Predicate goesOnRoadTripWith P90206 FINISHED
Object Walid Nadeem 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: Walid Nadeem | Statement: [Frank Bledsoe, goesOnRoadTripWith, Walid Nadeem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walid Nadeem
Context triple: [Frank Bledsoe, goesOnRoadTripWith, Walid Nadeem]
  • A. Walid Nadeem chosen
    Walid Nadeem is a fictional character known primarily as the romantic partner of Frank Bledsoe in the film "Uncle Frank."
  • B. Imad Wasif
    Imad Wasif is a Canadian singer-songwriter and guitarist known for his solo work in psychedelic and folk-influenced rock as well as collaborations with various indie rock bands.
  • C. Khalid Ashraf
    Khalid Ashraf is a computer scientist and deep learning researcher known for co-designing the efficient convolutional neural network architecture SqueezeNet.
  • D. Moeed Yusuf
    Moeed Yusuf is a Pakistani academic and policy expert who has served as the country’s National Security Adviser, focusing on strategic, security, and foreign policy issues.
  • E. Salim Akil
    Salim Akil is an American television and film director, writer, and producer best known for his work on projects like the TV series "Girlfriends," "The Game," and "Black Lightning."
  • 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_69e0b4ce39108190a6e8e5df4f1c8dc5 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2f6a65481909a0df78616e185e4 completed April 21, 2026, 12:21 a.m.
Created at: April 16, 2026, 12:41 p.m.