Triple

T2745608
Position Surface form Disambiguated ID Type / Status
Subject Harold Lee E60858 entity
Predicate bestFriend P8712 FINISHED
Object Kumar Patel E294664 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: Kumar Patel | Statement: [Harold Lee, bestFriend, Kumar Patel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kumar Patel
Context triple: [Harold Lee, bestFriend, Kumar Patel]
  • A. Kumar Patel chosen
    Kumar Patel is a laid-back, marijuana-loving Korean American character from the "Harold & Kumar" comedy film series, known for his misadventurous escapades with his best friend Harold Lee.
  • B. Sanjiv Singh
    Sanjiv Singh is a robotics researcher and professor known for his work in autonomous systems and field robotics at Carnegie Mellon University.
  • C. Rasesh Bhatt
    Rasesh Bhatt is known primarily as the husband of renowned Indian cooperative organizer and SEWA founder Ela Bhatt.
  • D. Ravi Bhalla
    Ravi Bhalla is an American attorney and politician who became the first Sikh mayor of Hoboken, New Jersey, and one of the first turbaned Sikh mayors in the United States.
  • E. Sanjay Jain
    Sanjay Jain is an economist recognized for his academic contributions and scholarship associated with the Delhi School of Economics.
  • 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_69ab4b79846081909096725374d65ce9 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb4d37a481908cc2ad4666f3ac94 completed March 7, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc6439b348190be7529256d9d0531 completed March 10, 2026, 7:20 a.m.
Created at: March 6, 2026, 9:56 p.m.