Triple

T10463190
Position Surface form Disambiguated ID Type / Status
Subject The Ipcress File E246726 entity
Predicate followedBy P78 FINISHED
Object Funeral in Berlin E478152 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: Funeral in Berlin | Statement: [The Ipcress File, followedBy, Funeral in Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Funeral in Berlin
Context triple: [The Ipcress File, followedBy, Funeral in Berlin]
  • A. Funeral in Berlin chosen
    Funeral in Berlin is a 1966 British Cold War spy film starring Michael Caine as secret agent Harry Palmer, adapted from Len Deighton’s novel of the same name.
  • B. Berlin Alexanderplatz
    Berlin Alexanderplatz is a major public square and transport hub in central Berlin, known for its shopping areas, historic sites, and proximity to the iconic TV Tower.
  • C. Mon enfant de Berlin
    Mon enfant de Berlin is a semi-autobiographical novel by Anne Wiazemsky that recounts a young French woman's experiences and personal awakening in post-World War II Berlin.
  • D. Galgenlieder
    Galgenlieder is a famous collection of humorous and linguistically playful nonsense poems by German writer Christian Morgenstern.
  • E. Death at a Funeral
    Death at a Funeral is a 2007 British black comedy film centered on a dysfunctional family gathering for a chaotic and farcical funeral.
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50884fac48190af22e181b1492557 completed April 7, 2026, 1:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69d89fd865688190b0b5708481f397f4 completed April 10, 2026, 6:59 a.m.
Created at: April 6, 2026, 12:19 p.m.