Triple

T3297267
Position Surface form Disambiguated ID Type / Status
Subject Abruzzo E69244 entity
Predicate hasProvince P285 FINISHED
Object Chieti E208665 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: Chieti | Statement: [Abruzzo, hasProvince, Chieti]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chieti
Context triple: [Abruzzo, hasProvince, Chieti]
  • A. Chieti chosen
    Chieti is an ancient city in the Abruzzo region of central Italy, known for its Roman archaeological sites and medieval architecture.
  • B. Pescara
    Pescara is a coastal city in the Abruzzo region of central Italy, known for its Adriatic beaches, modern urban layout, and role as a commercial and tourist hub.
  • C. Teramo
    Teramo is a historic city in the Abruzzo region of central Italy, known for its Roman archaeological remains and medieval architecture.
  • D. Terni
    Terni is an industrial city in the Umbria region of central Italy, known for its steel production and historic Roman and medieval heritage.
  • E. Foggia
    Foggia is a city in the Apulia region of southern Italy, historically significant as a medieval center and later as an important agricultural and commercial hub.
  • 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_69ad859e529c8190a404273f53cb487d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb078f3dc8190afb624f62894e48f completed March 8, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fae497588190b8f4d23d2925ad9b completed March 14, 2026, 6:06 a.m.
Created at: March 8, 2026, 3:10 p.m.