Triple

T17143623
Position Surface form Disambiguated ID Type / Status
Subject TGV Est E416032 entity
Predicate connectsCity P4245 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: [TGV Est, connectsCity, Munich]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich
Context triple: [TGV Est, connectsCity, Munich]
  • A. 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.
  • B. 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.
  • 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_69d886d15af4819092f92f8a129763e6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f2d73c3c81908b875023bb925edb completed April 18, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a015fc263508190aeeb0ea9553cebdb completed May 11, 2026, 4:49 a.m.
Created at: April 10, 2026, 5:36 a.m.