Triple

T9956487
Position Surface form Disambiguated ID Type / Status
Subject Colosseo metro station E195458 entity
Predicate partOfNetwork P840 FINISHED
Object Rome Metro E601065 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: Rome Metro | Statement: [Colosseo metro station, partOfNetwork, Rome Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rome Metro
Context triple: [Colosseo metro station, partOfNetwork, Rome Metro]
  • A. Rome Metro network chosen
    The Rome Metro network is the rapid transit system serving Italy’s capital, consisting of multiple underground and suburban rail lines that connect central Rome with its surrounding districts and transport hubs.
  • B. Rome Metro Line B
    Rome Metro Line B is one of the main lines of the Rome Metro system, running through central and southern parts of the city and serving key sites such as the Colosseum.
  • C. Turin Metro
    The Turin Metro is a fully automated, driverless rapid transit system serving the city of Turin, Italy.
  • D. Milan Metro
    The Milan Metro is the rapid transit system serving Milan, Italy, forming the backbone of the city’s public transportation network with multiple underground lines connecting central and suburban areas.
  • E. Brescia Metro
    Brescia Metro is a fully automated light metro system serving the city of Brescia in northern Italy.
  • 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_69ca82eaaa008190a54fa1a9f954b9ad completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb6976b50819097a0ae347354e92c completed April 2, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69d257aa73d4819081f77f8386449905 completed April 5, 2026, 12:38 p.m.
Created at: March 30, 2026, 8:46 p.m.