Triple

T14682991
Position Surface form Disambiguated ID Type / Status
Subject MTU München E344833 entity
Predicate basedIn P40 FINISHED
Object Munich E21335 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: Munich | Statement: [MTU München, basedIn, Munich]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich
Context triple: [MTU München, basedIn, Munich]
  • A. Munich chosen
    Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
  • B. Munich
    "Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
  • C. Munychia
    Munychia was a fortified hill and harbor district of ancient Athens, in Piraeus, known as the site where the oligarchic regime of the Thirty Tyrants was overthrown.
  • D. Leverkusen
    Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
  • E. Regensburg
    Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb56a51ec8190941684fd562a7182 completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe64ea085c8190b308504fa11c731d completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:28 a.m.