Triple

T4293672
Position Surface form Disambiguated ID Type / Status
Subject A3C E99656 entity
Predicate introducedBy P513 FINISHED
Object Mehdi Mirza
Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
 E428320 NE FINISHED

How this triple was built (4 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: Mehdi Mirza | Statement: [A3C, introducedBy, Mehdi Mirza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mehdi Mirza
Context triple: [A3C, introducedBy, Mehdi Mirza]
  • A. Rasoul Azadani
    Rasoul Azadani is a film cinematographer best known for his work on Disney’s animated feature "Tangled."
  • B. Saeed Sohrab
    Saeed Sohrab is an Iranian academic and mathematician recognized as a distinguished alumnus of Sharif University of Technology.
  • C. 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.
  • D. Zekeria Ebrahimi
    Zekeria Ebrahimi is an Afghan actor best known for his role as the young Amir in the film adaptation of "The Kite Runner."
  • E. David Bakhtiari
    David Bakhtiari is an American football offensive tackle best known for his Pro Bowl career with the Green Bay Packers in the NFL.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mehdi Mirza
Triple: [A3C, introducedBy, Mehdi Mirza]
Generated description
Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mehdi Mirza
Target entity description: Mehdi Mirza is a machine learning researcher known for his contributions to deep reinforcement learning and generative models.
  • A. Rasoul Azadani
    Rasoul Azadani is a film cinematographer best known for his work on Disney’s animated feature "Tangled."
  • B. Saeed Sohrab
    Saeed Sohrab is an Iranian academic and mathematician recognized as a distinguished alumnus of Sharif University of Technology.
  • C. 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.
  • D. Zekeria Ebrahimi
    Zekeria Ebrahimi is an Afghan actor best known for his role as the young Amir in the film adaptation of "The Kite Runner."
  • E. David Bakhtiari
    David Bakhtiari is an American football offensive tackle best known for his Pro Bowl career with the Green Bay Packers in the NFL.
  • F. None of above. chosen

Provenance (5 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35082228081908504e3fd7c4ca1e8 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c73d47448190a844bc13eae84a54 completed March 14, 2026, 8:38 p.m.
NEDg Description generation batch_69b5c7d04508819087b14c5c86f1e015 completed March 14, 2026, 8:40 p.m.
NED2 Entity disambiguation (via description) batch_69b5c84ccea08190a8e7e8fa93934ea2 completed March 14, 2026, 8:42 p.m.
Created at: March 12, 2026, 11:08 p.m.