Triple

T9718341
Position Surface form Disambiguated ID Type / Status
Subject Taba Heights resort area E235398 entity
Predicate nearestCity P350 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: [Taba Heights resort area, nearestCity, Taba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taba
Context triple: [Taba Heights resort area, nearestCity, 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. Tabary
    Tabary was a French comics artist best known for co-creating and illustrating the humorous series "Iznogoud" in collaboration with writer René Goscinny.
  • D. Anseba
    Anseba is a central region of Eritrea known for its diverse ethnic communities, agriculture, and the regional capital Keren.
  • E. Buhera
    Buhera is a rural town and district center in eastern Zimbabwe known for its agricultural activities and location within Manicaland Province.
  • 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_69ca84d0123c819096f9dc3b6abb0881 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e3ea61081908a5671fc5be9a738 completed April 1, 2026, 10:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f986f48819090376fb5aafb3da2 completed April 4, 2026, 11:32 p.m.
Created at: March 30, 2026, 8:20 p.m.