Triple

T16025874
Position Surface form Disambiguated ID Type / Status
Subject Mount Voras E388715 entity
Predicate hasRoadAccessFrom P22549 FINISHED
Object Edessa E105315 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: [Mount Voras, hasRoadAccessFrom, Edessa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Edessa
Context triple: [Mount Voras, hasRoadAccessFrom, Edessa]
  • A. Edessa chosen
    Edessa is a historic city in northern Greece renowned for its picturesque waterfalls and ancient heritage.
  • B. Edessa
    Edessa was an ancient city in Upper Mesopotamia, renowned as a major early center of Syriac Christianity and culture.
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1832790548190a74045d554e13328 completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffcf31c8d8819096c562ba1453f3c0 completed May 10, 2026, 12:20 a.m.
Created at: April 10, 2026, 4:56 a.m.