Triple

T14996477
Position Surface form Disambiguated ID Type / Status
Subject Kol E373969 entity
Predicate hasNotableBearer P458 FINISHED
Object Moshe Kol E76470 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: Moshe Kol | Statement: [Kol, hasNotableBearer, Moshe Kol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moshe Kol
Context triple: [Kol, hasNotableBearer, Moshe Kol]
  • A. Moshe Kol chosen
    Moshe Kol was an Israeli politician and Zionist leader who served as a signatory of Israel’s Declaration of Independence and later as a government minister, particularly known for his work in social welfare and immigrant absorption.
  • B. Moshe Aviv
    Moshe Aviv was an Israeli businessman and real estate developer best known for his major role in shaping modern Israeli urban skylines.
  • C. Moshe Baram
    Moshe Baram was an Israeli politician and member of the Alignment/Labor Party who served as a government minister and Knesset member in the 1960s and 1970s.
  • D. Moshe Rosen
    Moshe Rosen was an Israeli scientist and academic known for supervising and mentoring future Nobel laureate Dan Shechtman during his doctoral studies.
  • E. Moshe Edery
    Moshe Edery is an Israeli film producer and distributor known for his significant role in the Israeli cinema industry.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded718e4288190b5e144f82299a194 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fec8741c048190b549782f49969f6a completed May 9, 2026, 5:39 a.m.
Created at: April 10, 2026, 2:53 a.m.