Triple

T7548868
Position Surface form Disambiguated ID Type / Status
Subject Weinheim E178475 entity
Predicate hasTwinTown P919 FINISHED
Object Ramat Gan E19003 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: Ramat Gan | Statement: [Weinheim, hasTwinTown, Ramat Gan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ramat Gan
Context triple: [Weinheim, hasTwinTown, Ramat Gan]
  • A. Ramat Gan chosen
    Ramat Gan is a city in the Tel Aviv District of Israel, known for its diamond exchange district, business centers, and large urban park.
  • B. 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.
  • C. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • D. Hadera
    Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
  • E. 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.
  • 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_69c69f2cbe08819088f9eb0c03ef529b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f89b9afc8190b3e61a8e2cea7ad7 completed March 27, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86836bf588190aa1b4104c2d06a1f completed March 28, 2026, 11:45 p.m.
Created at: March 27, 2026, 3:49 p.m.