Triple

T13968848
Position Surface form Disambiguated ID Type / Status
Subject Tel Aviv–Haifa–Nahariya line E335998 entity
Predicate connectsCity P4245 FINISHED
Object Netanya E445634 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: Netanya | Statement: [Tel Aviv–Haifa–Nahariya line, connectsCity, Netanya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Netanya
Context triple: [Tel Aviv–Haifa–Nahariya line, connectsCity, Netanya]
  • A. Netanya chosen
    Netanya is a coastal city in central Israel on the Mediterranean Sea, known for its beaches, tourism, and role as a regional economic center.
  • B. 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.
  • C. Ness Ziona
    Ness Ziona is a small city in central Israel known for its scientific research institutions and proximity to Tel Aviv.
  • D. Hadera
    Hadera is a coastal city in northern Israel known for its power station, beaches, and location between Tel Aviv and Haifa.
  • E. Herzliya
    Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
  • 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_69d81c61f3508190aaf2ca0dc0002c59 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2e8daeac8190aadd4b3b60222482 completed April 14, 2026, 12:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69feb7cccd548190b9ec93f9d9d2f2af completed May 9, 2026, 4:27 a.m.
Created at: April 9, 2026, 10:18 p.m.