Triple

T16886513
Position Surface form Disambiguated ID Type / Status
Subject Heidi (2015 film) E421553 entity
Predicate setting P1957 FINISHED
Object Frankfurt am Main E16481 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: Frankfurt am Main | Statement: [Heidi (2015 film), setting, Frankfurt am Main]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt am Main
Context triple: [Heidi (2015 film), setting, Frankfurt am Main]
  • A. Frankfurt am Main chosen
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • B. Frankfurt Rhine-Main
    Frankfurt Rhine-Main is a major metropolitan region in western Germany centered around Frankfurt am Main, known as a key European hub for finance, transportation, and commerce.
  • C. Wiesbaden
    Wiesbaden is a historic spa city in western Germany known for its thermal springs, elegant architecture, and role as a regional administrative and cultural center.
  • D. Mannheim
    Mannheim is a major city in southwestern Germany, known as an important industrial, commercial, and cultural center at the confluence of the Rhine and Neckar rivers.
  • E. Cologne
    Cologne is a historic German city on the Rhine River, renowned for its Gothic cathedral, vibrant cultural scene, and status as a major economic and media hub.
  • 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_69d889d470fc8190b4aec199636c0c56 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3bbc126e881909dae8133ad34acc9 completed April 18, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d447f5cc81908757869f2d1e94a1 completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:29 a.m.