Triple

T6014559
Position Surface form Disambiguated ID Type / Status
Subject Avraham Krinitzi E133916 entity
Predicate workLocation P7 FINISHED
Object Ramat Gan E19003 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: Ramat Gan | Statement: [Avraham Krinitzi, workLocation, Ramat Gan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ramat Gan
Context triple: [Avraham Krinitzi, workLocation, Ramat Gan]
  • A. Ramat Gan chosen
    Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
  • B. Petah Tikva
    Petah Tikva is a major city in central Israel, known as one of the country’s oldest modern Jewish settlements and a significant industrial and commercial hub in the Tel Aviv metropolitan area.
  • C. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • D. Hadera
    Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
  • E. Ra'anana
    Ra'anana is a prosperous suburban city in central Israel known for its high quality of life, strong education system, and significant high-tech and business presence.
  • 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_69c0087361a48190905c6b55969852b8 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04f544fd88190b6777c4db04e274f completed March 22, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6409d48408190b2048c07272277ac completed March 27, 2026, 8:32 a.m.
Created at: March 22, 2026, 4:06 p.m.