Triple

T7495777
Position Surface form Disambiguated ID Type / Status
Subject Chacabuco Province E177123 entity
Predicate containsCommune P15149 FINISHED
Object Lampa E145225 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: Lampa | Statement: [Chacabuco Province, containsCommune, Lampa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lampa
Context triple: [Chacabuco Province, containsCommune, Lampa]
  • A. Lampa chosen
    Lampa is a commune and town in central Chile known for its semi-rural character and growing residential and industrial development near Santiago.
  • B. Lampada Ferens
    Lampada Ferens is the Latin motto of the University of Hull, traditionally translated as "carrying the light" or "bearing the lamp."
  • C. Lampione
    Lampione is a tiny, uninhabited rocky islet in the Mediterranean Sea, part of Italy’s Pelagie Islands and known for its rich marine life and popular diving spots.
  • D. Abajur
    Abajur is a conceptual artwork by Brazilian artist Cildo Meireles, known for its exploration of perception, space, and political or social commentary through immersive, often installation-based forms.
  • E. Lumo
    Lumo is a British open-access train operator running low-cost, long-distance electric services on the East Coast Main Line between London and northeastern England.
  • 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_69c69f2583808190bd1a4936c42a5815 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f57c86948190aa8ee765bd497850 completed March 27, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c8686cc8190bb1f7b09cdbebcf7 completed March 28, 2026, 8:39 p.m.
Created at: March 27, 2026, 3:43 p.m.