Triple

T2437575
Position Surface form Disambiguated ID Type / Status
Subject Wolf Prize E53195 entity
Predicate headquartersLocation P62 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: [Wolf Prize, headquartersLocation, Herzliya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herzliya
Context triple: [Wolf Prize, headquartersLocation, 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. 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.
  • D. Kiryat Ono
    Kiryat Ono is a small suburban city in central Israel, located in the Tel Aviv metropolitan area.
  • E. Rehovot
    Rehovot is a city in central Israel known for its scientific and agricultural research institutions, including the Weizmann Institute of Science.
  • 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_69ab495b6dac8190ac82661aa1452222 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc9f4d2dc8190b3c264a6c20d1bd5 completed March 7, 2026, 6:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf32014c8190aa3cfea010c3bb39 completed March 10, 2026, 5:42 a.m.
Created at: March 6, 2026, 9:43 p.m.