Triple

T17352373
Position Surface form Disambiguated ID Type / Status
Subject Bir-Hakeim (Paris Métro) E421844 entity
Predicate nearbyLandmark P350 FINISHED
Object Eiffel Tower NE ONNED1

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: [Bir-Hakeim (Paris Métro), nearbyLandmark, Eiffel Tower]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eiffel Tower
Context triple: [Bir-Hakeim (Paris Métro), nearbyLandmark, 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. Iron Tower
    The Iron Tower is a historic medieval watchtower and former city gate located in Mainz, Germany.
  • E. 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.
  • 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a2dae648190b7f3487919a446af completed April 19, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01955a50dc819090c1a0ec111d9fc0 in_progress May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:44 a.m.