Triple

T10375988
Position Surface form Disambiguated ID Type / Status
Subject APRR E244508 entity
Predicate ownsSubsidiary P9212 FINISHED
Object AREA E244509 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: AREA | Statement: [APRR, ownsSubsidiary, AREA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AREA
Context triple: [APRR, ownsSubsidiary, AREA]
  • A. AREA chosen
    AREA is a French motorway concession company responsible for operating and maintaining part of the national autoroute network.
  • B. L Area
    L Area is a reactor and support complex within the U.S. Department of Energy’s Savannah River Site, historically used for plutonium and tritium production and related nuclear operations.
  • C. Areal
    Areal is a small municipality in the mountainous Região Serrana of Rio de Janeiro state in southeastern Brazil.
  • D. INT Area
    The INT Area is the Internet Area of the IETF, responsible for developing and maintaining protocols and architectures related to the core Internet infrastructure and IP-based internetworking.
  • E. Abzhywa area
    The Abzhywa area is a historical region traditionally inhabited by Abkhaz people, known as the homeland of the Abzhywa dialect.
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e98278e08190a4d3ff88b4039e49 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d795754570819093747f80e32fffce completed April 9, 2026, 12:03 p.m.
Created at: April 6, 2026, 12:02 p.m.