Triple

T27419833
Position Surface form Disambiguated ID Type / Status
Subject Fritz Rasp E693007 entity
Predicate portrayedCharacterIn P1668 FINISHED
Object The Thin Man in Metropolis
The Thin Man in Metropolis is a mysterious, sharp-featured agent of the ruling elite who spies on and manipulates others in Fritz Lang’s 1927 science-fiction film "Metropolis."
 E1771658 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: The Thin Man in Metropolis | Statement: [Fritz Rasp, portrayedCharacterIn, The Thin Man in Metropolis]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: The Thin Man in Metropolis
Triple: [Fritz Rasp, portrayedCharacterIn, The Thin Man in Metropolis]
Generated description
The Thin Man in Metropolis is a mysterious, sharp-featured agent of the ruling elite who spies on and manipulates others in Fritz Lang’s 1927 science-fiction film "Metropolis."

Provenance (5 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_69ef5208617081908f731d312e0fd1bc completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d1d19848190b079613616d2e407 completed May 2, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b2483f30819098bf33b785f64295 completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b41d20008190a453cd775e7f8240 completed May 24, 2026, 8:17 a.m.
NED2 Entity disambiguation (via description) batch_6a12b4da86248190ae6ff993c5b904df completed May 24, 2026, 8:20 a.m.
Created at: April 27, 2026, 12:35 p.m.