Triple

T2611044
Position Surface form Disambiguated ID Type / Status
Subject Marguerite Gaudelet E58773 entity
Predicate spouseNotableWork P19181 FINISHED
Object Eiffel Tower E1351 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: Eiffel Tower | Statement: [Marguerite Gaudelet, spouseNotableWork, Eiffel Tower]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eiffel Tower
Context triple: [Marguerite Gaudelet, spouseNotableWork, Eiffel Tower]
  • A. Eiffel Tower chosen
    The Eiffel Tower is a wrought-iron lattice tower in Paris, France, and one of the most recognizable landmarks and symbols of the country.
  • B. Eiffel
    Eiffel is a French surname most famously associated with engineer Gustave Eiffel, designer of the Eiffel Tower in Paris.
  • C. Eiffel
    Eiffel is an object-oriented programming language designed by Bertrand Meyer, known for its emphasis on software correctness through the Design by Contract methodology.
  • D. 58 Tour Eiffel
    58 Tour Eiffel is a contemporary French restaurant located on the first floor of the Eiffel Tower, offering panoramic views of Paris.
  • E. Trocadéro
    Trocadéro is a prominent area in Paris known for its grand esplanade and panoramic views of the Eiffel Tower, historically associated with major exhibitions and cultural events.
  • 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_69ab4ac3523881909679750c9f8c2dec completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd87b24e48190ad1d4ce7e63c0f3e completed March 7, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69af83e506188190b6cd3b507dfc353c completed March 10, 2026, 2:37 a.m.
Created at: March 6, 2026, 9:50 p.m.