Triple

T1652351
Position Surface form Disambiguated ID Type / Status
Subject Göppingen E35718 entity
Predicate hasTwinTown P919 FINISHED
Object Foggia E211126 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: Foggia | Statement: [Göppingen, hasTwinTown, Foggia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Foggia
Context triple: [Göppingen, hasTwinTown, Foggia]
  • A. Foggia chosen
    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.
  • B. Chieti
    Chieti is an ancient city in the Abruzzo region of central Italy, known for its Roman archaeological sites and medieval architecture.
  • C. 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.
  • D. Brindisi
    Brindisi is a historic port city in southern Italy’s Apulia region, long serving as a key maritime gateway between Italy and the eastern Mediterranean.
  • E. Teramo
    Teramo is a historic city in the Abruzzo region of central Italy, known for its Roman archaeological remains and medieval architecture.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a88cb108190a836b972f600c257 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae303cdce081909b66ec04de43cf53 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:29 p.m.