Triple

T9291329
Position Surface form Disambiguated ID Type / Status
Subject Tourcoing E223523 entity
Predicate twinTown P1072 FINISHED
Object Rosh HaAyin E647332 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: Rosh HaAyin | Statement: [Tourcoing, twinTown, Rosh HaAyin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rosh HaAyin
Context triple: [Tourcoing, twinTown, Rosh HaAyin]
  • A. Rosh HaAyin chosen
    Rosh HaAyin is a city in central Israel known for its rapid development, diverse population, and proximity to major transportation routes and employment centers.
  • B. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • C. Givatayim
    Givatayim is a small, densely populated city in Israel’s Tel Aviv metropolitan area, known for its residential character and proximity to major urban centers.
  • 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_69ca8422ddf881908a3f8f876c9f53aa completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd0865a7108190b807afd259980db2 completed April 1, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0b2381e2c8190acbecf97dea1200c completed April 4, 2026, 6:39 a.m.
Created at: March 30, 2026, 7:35 p.m.