Triple

T12305366
Position Surface form Disambiguated ID Type / Status
Subject Herzliya Conference E293338 entity
Predicate location P40 FINISHED
Object Herzliya E60622 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: Herzliya | Statement: [Herzliya Conference, location, Herzliya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herzliya
Context triple: [Herzliya Conference, location, Herzliya]
  • A. Herzliya chosen
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • 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. Rishon LeZion
    Rishon LeZion is one of Israel’s largest and oldest cities, located on the country’s central coastal plain and known for its historical role in the early Zionist movement and modern urban development.
  • D. Hadera
    Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
  • E. 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.
  • 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_69d6ab6a2b50819082f6aedd32ed608a completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f00695c8190b7365e4593631690 completed April 10, 2026, 6:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7c93f048190a755addc0922064b completed May 7, 2026, 8:36 p.m.
Created at: April 8, 2026, 9:53 p.m.