Triple

T3108695
Position Surface form Disambiguated ID Type / Status
Subject Yehuda Leib Maimon E64896 entity
Predicate residence P75 FINISHED
Object Tel Aviv E11499 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: Tel Aviv | Statement: [Yehuda Leib Maimon, residence, Tel Aviv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tel Aviv
Context triple: [Yehuda Leib Maimon, residence, Tel Aviv]
  • A. Tel Aviv chosen
    Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
  • B. Ramat Gan
    Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
  • C. Tel Aviv metropolitan area
    The Tel Aviv metropolitan area is Israel’s largest urban and economic hub, centered on the city of Tel Aviv and encompassing numerous surrounding municipalities along the Mediterranean coast.
  • D. 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.
  • E. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada29eacc88190a19c5ca8e53e3dca completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20357f8c48190b6874f7596f30052 completed March 12, 2026, 12:05 a.m.
Created at: March 8, 2026, 3:04 p.m.