Triple

T21855556
Position Surface form Disambiguated ID Type / Status
Subject Banias E539614 entity
Predicate hasNearbyCity P350 FINISHED
Object Latakia NE NERFINISHED

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: Latakia | Statement: [Banias, hasNearbyCity, Latakia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Latakia
Context triple: [Banias, hasNearbyCity, Latakia]
  • A. Latakia chosen
    Latakia is a major port city on Syria's Mediterranean coast and an important economic and cultural center for the country.
  • B. Tartus
    Tartus is a major Syrian port city on the Mediterranean coast that hosts Russia’s only naval facility outside the former Soviet Union.
  • C. Aleppo
    Aleppo is an ancient and historically significant city in northern Syria, renowned for its rich cultural heritage, medieval architecture, and role as a major trading hub along the Silk Road.
  • D. Homs
    Homs is one of Syria’s largest and oldest cities, historically a major commercial and industrial center located in the western part of the country.
  • E. Raqqa
    Raqqa is a city in northern Syria that became widely known as the de facto capital of the Islamic State (ISIS) during its control from 2014 to 2017.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c47829648190bbe2d1d7033768ec completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f0d635b59c81908810480f3802b847 completed April 28, 2026, 3:45 p.m.
Created at: April 16, 2026, 6:56 p.m.