Triple

T21603789
Position Surface form Disambiguated ID Type / Status
Subject Empirical Inference Department E533116 entity
Predicate locatedIn P40 FINISHED
Object Tübingen NE NERFINISHED

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: Tübingen | Statement: [Empirical Inference Department, locatedIn, Tübingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tübingen
Context triple: [Empirical Inference Department, locatedIn, Tübingen]
  • A. Tübingen chosen
    Tübingen is a historic university town in southwestern Germany known for its well-preserved medieval old town and prestigious Eberhard Karls University.
  • B. Tüfingen
    Tüfingen was a former locality in what is now Salem, Baden-Württemberg, Germany, whose identity was incorporated into the larger municipality through administrative reorganization.
  • C. Heidelberg
    Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
  • D. Heidelberg
    Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
  • E. Heidelberg
    Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c46364608190a337dc8720dc2a35 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef17e3e18c8190b9e0626c7d16805b completed April 27, 2026, 8:01 a.m.
Created at: April 16, 2026, 6:33 p.m.