Triple

T12620405
Position Surface form Disambiguated ID Type / Status
Subject Mieresch E301362 entity
Predicate hasAlternativeName P39 FINISHED
Object Maros E555091 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: Maros | Statement: [Mieresch, hasAlternativeName, Maros]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maros
Context triple: [Mieresch, hasAlternativeName, Maros]
  • A. Maros
    Maros is the historical name of the Mureș River, a major waterway flowing through central and eastern Europe, particularly present-day Romania and Hungary.
  • B. Maros chosen
    Maros is a regency in South Sulawesi, Indonesia, known for its proximity to Makassar and its role as a regional transport hub and gateway via Sultan Hasanuddin International Airport.
  • C. Watampone
    Watampone is the main urban and administrative center of Bone Regency in South Sulawesi, Indonesia.
  • D. Melawi
    Melawi is a regency-level administrative area in the interior of West Kalimantan, Indonesia, known for its riverine landscapes and predominantly Dayak communities.
  • E. Pakpak-Dairi
    Pakpak-Dairi is an Austronesian language spoken by the Pakpak (Dairi) people of northern Sumatra, Indonesia.
  • 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_69d7bdeaf49c8190b13800111fa77ea3 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d960c75c9c819092265ebc2b39f21d completed April 10, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ed507988190b9d46586f3c3584c completed May 2, 2026, 8:30 p.m.
Created at: April 9, 2026, 5:13 p.m.