Triple

T11372746
Position Surface form Disambiguated ID Type / Status
Subject Autostrada A10 E269382 entity
Predicate connectsCity P4245 FINISHED
Object Sanremo E25039 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: Sanremo | Statement: [Autostrada A10, connectsCity, Sanremo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sanremo
Context triple: [Autostrada A10, connectsCity, Sanremo]
  • A. Sanremo chosen
    Sanremo is a coastal city on Italy’s Ligurian Riviera, known as a historic resort destination and host of the annual Sanremo Music Festival.
  • B. San Remigio
    San Remigio is a coastal municipality in the province of Cebu in the Philippines, known for its long stretch of white-sand beaches and dive spots.
  • C. Poggio di Sanremo
    Poggio di Sanremo is a short but decisive late-race climb near the finish of the Milan–San Remo cycling classic, often shaping the outcome of the race.
  • D. Bordighera
    Bordighera is a coastal town on the Italian Riviera in Liguria, known for its mild climate, palm-lined seafront, and historic appeal as a 19th-century resort.
  • E. Lerici
    Lerici is a picturesque coastal town on Italy’s Ligurian Riviera, known for its historic castle, scenic harbor, and literary associations with poets like Percy Bysshe Shelley.
  • 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_69d6aacca1048190b39dbbc2174616fa completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea8d244c8190b865260338edb532 completed April 9, 2026, 6:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5568516a481909ea66cbe53968e84 completed April 19, 2026, 10:26 p.m.
Created at: April 8, 2026, 9:33 p.m.