Triple

T7394219
Position Surface form Disambiguated ID Type / Status
Subject Synopsys E170579 entity
Predicate hasRegionalHeadquarters P40187 FINISHED
Object Munich, Germany 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, Germany | Statement: [Synopsys, hasRegionalHeadquarters, Munich, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munich, Germany
Context triple: [Synopsys, hasRegionalHeadquarters, Munich, Germany]
  • 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. Leverkusen
    Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
  • C. Brühl, Germany
    Brühl, Germany is a town in North Rhine-Westphalia known for its UNESCO-listed Augustusburg and Falkenlust palaces and its proximity to Cologne.
  • D. Deggendorf, Germany
    Deggendorf, Germany is a Bavarian town on the Danube River known as a regional commercial and industrial center with strong ties to manufacturing and technology companies.
  • E. Landsberg am Lech, Germany
    Landsberg am Lech is a historic Bavarian town in southern Germany known for its well-preserved medieval old town and picturesque location along the Lech 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_69c68a5f04188190ac266569c9280347 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f2263b48819089319a2a2f0d3357 completed March 27, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c4dc1c08190876eb0e70f387b77 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 3:09 p.m.