Triple

T6301327
Position Surface form Disambiguated ID Type / Status
Subject Iraq–Saudi Arabia border area E141260 entity
Predicate nearbyCity P350 FINISHED
Object Arar E196368 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: Arar | Statement: [Iraq–Saudi Arabia border area, nearbyCity, Arar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arar
Context triple: [Iraq–Saudi Arabia border area, nearbyCity, Arar]
  • A. Arar chosen
    Arar is a city in northern Saudi Arabia that serves as the capital of the Northern Borders Region and a regional center for trade and administration.
  • B. Nahe
    Nahe is a renowned German wine region, particularly celebrated for producing high-quality Riesling wines with diverse styles due to its varied soils and microclimates.
  • C. Avre
    The Avre is a river in northern France that serves as a tributary of the Eure, flowing through the Normandy and Centre-Val de Loire regions.
  • D. Rissne
    Rissne is a residential district and urban area in the Stockholm metropolitan region of Sweden, known for its mix of apartment housing and proximity to public transit.
  • E. Kawa
    Kawa is a programming language framework that compiles Scheme and other high-level languages to Java bytecode for execution on the Java Virtual Machine.
  • 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_69c008cf0ad4819095def81e2bd42f9f completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0645bb41481909294b06e2b3e1845 completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e42f38bc819086a3e66a83ffc792 completed March 27, 2026, 1:58 a.m.
Created at: March 22, 2026, 4:27 p.m.