Triple

T3358288
Position Surface form Disambiguated ID Type / Status
Subject Incredibles 2 E70656 entity
Predicate cinematographyBy P1953 FINISHED
Object Mahyar Abousaeedi E256025 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: Mahyar Abousaeedi | Statement: [Incredibles 2, cinematographyBy, Mahyar Abousaeedi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mahyar Abousaeedi
Context triple: [Incredibles 2, cinematographyBy, Mahyar Abousaeedi]
  • A. Mahyar Abousaeedi chosen
    Mahyar Abousaeedi is a cinematographer best known for his work on the Pixar animated film "Turning Red."
  • B. Karim Khalili
    Karim Khalili is an Afghan politician and former vice president who served as a prominent Hazara leader and key figure in the anti-Taliban resistance.
  • C. Hossein Amini
    Hossein Amini is an Iranian-British screenwriter and director known for his work on films such as "Drive," "The Wings of the Dove," and various literary adaptations.
  • D. Mohammad Mohaqiq
    Mohammad Mohaqiq is an Afghan Hazara political leader and former mujahideen commander who has played a prominent role in Afghanistan’s post-Taliban politics.
  • E. Babak Hassibi
    Babak Hassibi is an Iranian-American electrical engineer and information theorist known for his contributions to wireless communications, signal processing, and control theory, and for serving as a professor at the California Institute of Technology.
  • 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_69ad85a660c48190998489309a3b4869 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb26523cc819091006fde7beb32e4 completed March 8, 2026, 5:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3253e91988190bbfdafdf88ab0df1 completed March 12, 2026, 8:42 p.m.
Created at: March 8, 2026, 3:13 p.m.