Triple

T11855989
Position Surface form Disambiguated ID Type / Status
Subject Tel Aviv metropolitan area E282040 entity
Predicate hasMunicipality P847 FINISHED
Object Ness Ziona E526029 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: Ness Ziona | Statement: [Tel Aviv metropolitan area, hasMunicipality, Ness Ziona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ness Ziona
Context triple: [Tel Aviv metropolitan area, hasMunicipality, Ness Ziona]
  • A. Ness Ziona chosen
    Ness Ziona is a small city in central Israel known for its scientific research institutions and proximity to Tel Aviv.
  • B. 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.
  • C. Netanya
    Netanya is a coastal city in central Israel on the Mediterranean Sea, known for its beaches, tourism, and role as a regional economic center.
  • D. Kiryat Ono
    Kiryat Ono is a small suburban city in central Israel, located in the Tel Aviv metropolitan area.
  • E. 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.
  • 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_69d6ab287ba48190a5178779fd19b9b7 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a697f4108190af984932d2118472 completed April 10, 2026, 7:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69f6fef311c4819094b6b08a62d3afb3 completed May 3, 2026, 7:53 a.m.
Created at: April 8, 2026, 9:43 p.m.