Triple

T21335344
Position Surface form Disambiguated ID Type / Status
Subject Ness Ziona E526029 entity
Predicate locatedNear P294 FINISHED
Object Tel Aviv NE NERFINISHED

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: [Ness Ziona, locatedNear, Tel Aviv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tel Aviv
Context triple: [Ness Ziona, locatedNear, 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. Aviv
    Aviv is a Hebrew surname and given name meaning "spring," commonly used in Israel and among Jewish communities worldwide.
  • 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. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b51c33048190ab27cede74ef798c completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e898d6fcbc8190b83d9cfc9b4ca123 completed April 22, 2026, 9:45 a.m.
Created at: April 16, 2026, 4:43 p.m.