Triple

T9369384
Position Surface form Disambiguated ID Type / Status
Subject Operation Thunderbolt E225489 entity
Predicate planningBy P5245 FINISHED
Object Dan Shomron E795096 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: Dan Shomron | Statement: [Operation Thunderbolt, planningBy, Dan Shomron]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan Shomron
Context triple: [Operation Thunderbolt, planningBy, Dan Shomron]
  • A. Dan Shomron chosen
    Dan Shomron was an Israeli general who later became IDF Chief of Staff and is best known for planning and leading the 1976 Entebbe hostage-rescue raid.
  • B. Doron Peled
    Doron Peled is a computer scientist known for his contributions to formal methods and model checking, particularly in collaboration with Edmund M. Clarke.
  • C. Oren Uziel
    Oren Uziel is an American screenwriter and filmmaker known for genre-blending projects such as The Cloverfield Paradox and 22 Jump Street.
  • D. Uri Tadmor
    Uri Tadmor is a linguist known for his research on Austronesian languages, particularly the Lamaholot language of eastern Indonesia.
  • E. Amnon Yariv
    Amnon Yariv is an Israeli-American physicist and electrical engineer renowned for his pioneering contributions to lasers, optoelectronics, and photonics theory.
  • 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_69ca842cbddc819099d71ecec48cf9e5 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5080f55c8190bd5ca0dc0a4ea989 completed April 1, 2026, 5:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69d100d68d28819082f366f3b5493cfb completed April 4, 2026, 12:15 p.m.
Created at: March 30, 2026, 7:43 p.m.