Triple

T11060340
Position Surface form Disambiguated ID Type / Status
Subject King Abgar V of Osroene E261490 entity
Predicate capitalOfRealm P3877 FINISHED
Object Edessa E46023 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: Edessa | Statement: [King Abgar V of Osroene, capitalOfRealm, Edessa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Edessa
Context triple: [King Abgar V of Osroene, capitalOfRealm, Edessa]
  • A. Edessa chosen
    Edessa was an ancient city in Upper Mesopotamia, renowned as a major early center of Syriac Christianity and culture.
  • B. Edessa
    Edessa is a historic city in northern Greece renowned for its picturesque waterfalls and ancient heritage.
  • C. EDESSA
    EDESSA is the company responsible for managing and operating Estadio Cuscatlán, one of the largest and most important football stadiums in El Salvador.
  • D. Hierapolis
    Hierapolis was an ancient Greco-Roman city in Phrygia (modern-day Turkey), known for its hot springs and as an early center of Christianity.
  • E. Turkmenabat
    Turkmenabat is one of the largest cities in Turkmenistan, serving as an important industrial, transport, and cultural center in the country’s east near the border with Uzbekistan.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d798e991848190b07c2f48dae38681 completed April 9, 2026, 12:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4cbe9da2081908641b229bc2e648d completed April 19, 2026, 12:34 p.m.
Created at: April 8, 2026, 9:26 p.m.