Triple

T9151518
Position Surface form Disambiguated ID Type / Status
Subject District of Starnberg E219595 entity
Predicate hasMunicipality P847 FINISHED
Object Tutzing E209639 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: Tutzing | Statement: [District of Starnberg, hasMunicipality, Tutzing]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tutzing
Context triple: [District of Starnberg, hasMunicipality, Tutzing]
  • A. Tutzing chosen
    Tutzing is a Bavarian lakeside town in southern Germany known for its scenic location on Lake Starnberg and its role as a residential and resort community near Munich.
  • B. Tazmalt
    Tazmalt is a town and commune in northern Algeria known as a local commercial and transport hub within Béjaïa Province.
  • C. Tagüeña
    Tagüeña is a Spanish surname most notably associated with Manuel Tagüeña, a Republican military officer and physicist active during the Spanish Civil War.
  • D. Otumba
    Otumba is a town in central Mexico historically notable as the site of the Battle of Otumba during the Spanish conquest.
  • E. Tumeremo
    Tumeremo is a mining town in southeastern Venezuela known for its gold deposits and location within Bolívar State.
  • 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_69ca83e25418819093c6503deeaf30de completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca96cf4548190a3a45172f0e9d0ec completed April 1, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0545c5bb48190b889e6e9ef0448a5 completed April 3, 2026, 11:59 p.m.
Created at: March 30, 2026, 7:20 p.m.