Triple

T10987578
Position Surface form Disambiguated ID Type / Status
Subject Gush Dan E259669 entity
Predicate hasCoreCity P294 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: [Gush Dan, hasCoreCity, Tel Aviv]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tel Aviv
Context triple: [Gush Dan, hasCoreCity, 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. 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.
  • C. 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.
  • D. 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.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d787b3c8388190be77e95d56979a7f completed April 9, 2026, 11:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69e344e9c66c81909163cea6aa9276e0 completed April 18, 2026, 8:46 a.m.
Created at: April 8, 2026, 9:24 p.m.