Triple

T3491354
Position Surface form Disambiguated ID Type / Status
Subject French Riviera E73737 entity
Predicate hasCity P316 FINISHED
Object Monaco E18404 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: Monaco | Statement: [French Riviera, hasCity, Monaco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monaco
Context triple: [French Riviera, hasCity, Monaco]
  • A. Monaco chosen
    Monaco is a small sovereign city-state on the French Riviera known for its wealth, luxury tourism, and status as a major tax haven and gambling hub.
  • B. Andorra
    Andorra is a small, landlocked principality in the eastern Pyrenees between France and Spain, known for its mountainous terrain, tourism, and status as a tax haven.
  • C. San Marino
    San Marino is a small, landlocked microstate surrounded by Italy, known as one of the world’s oldest republics and a popular tourist destination.
  • D. San Marino
    San Marino is a small, affluent residential city in Los Angeles County, California, known for its high-ranking schools and the Huntington Library, Art Museum, and Botanical Gardens.
  • E. Luxembourg
    Luxembourg is a small, landlocked Western European country known for its prosperous economy, status as a major financial center, and role as a founding member of the European Union.
  • 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_69ad85cca8d4819088494e9f3340fab5 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbaa720c8190af47b052cc66c225 completed March 8, 2026, 6:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e6511b08190b67c353df53599b5 completed March 13, 2026, 3:03 a.m.
Created at: March 8, 2026, 3:18 p.m.