Triple

T4779847
Position Surface form Disambiguated ID Type / Status
Subject Israel–Egypt border E106145 entity
Predicate passesNearCity P3945 FINISHED
Object Taba E43804 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: Taba | Statement: [Israel–Egypt border, passesNearCity, Taba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taba
Context triple: [Israel–Egypt border, passesNearCity, Taba]
  • A. Taba chosen
    Taba is a small Egyptian resort town on the Red Sea near the border with Israel, known for its beaches, coral reefs, and role as a popular gateway between the two countries.
  • B. Qataban
    Qataban was an ancient South Arabian kingdom known for its incense trade and strategic position along key caravan routes in what is now Yemen.
  • C. Anseba
    Anseba is a central region of Eritrea known for its diverse ethnic communities, agriculture, and the regional capital Keren.
  • D. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • E. Tharwa
    Tharwa is a small rural village in the Australian Capital Territory, located south of Canberra near the Murrumbidgee River.
  • 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_69bd43f3074c8190937e7b0a457fe9f1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd658a86288190bc80651840ce6b18 completed March 20, 2026, 3:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43cecc748190a410c262aa2e4b98 completed March 21, 2026, 7:07 a.m.
Created at: March 20, 2026, 1:21 p.m.