Triple

T30437876
Position Surface form Disambiguated ID Type / Status
Subject Ágnes Hranitzky E774359 entity
Predicate coDirected P7732 FINISHED
Object The Man from London
The Man from London is a 2007 Hungarian–French film noir directed by Béla Tarr and Ágnes Hranitzky, adapted from a Georges Simenon novel about a railway switchman entangled in a crime after discovering a suitcase of stolen money.
 E1916144 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 Man from London | Statement: [Ágnes Hranitzky, coDirected, The Man from London]
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 Man from London
Triple: [Ágnes Hranitzky, coDirected, The Man from London]
Generated description
The Man from London is a 2007 Hungarian–French film noir directed by Béla Tarr and Ágnes Hranitzky, adapted from a Georges Simenon novel about a railway switchman entangled in a crime after discovering a suitcase of stolen money.

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_69f22492d2a88190995ce8745d9becaa completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68696b51481908e337f6734102fea completed May 2, 2026, 11:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2798bfa9cc8190a35586e8901a280c completed June 9, 2026, 4:38 a.m.
NEDg Description generation batch_6a279a5c8efc81908f425462ce572bed completed June 9, 2026, 4:45 a.m.
NED2 Entity disambiguation (via description) batch_6a279b384ef48190b6ba698c9e708f71 completed June 9, 2026, 4:48 a.m.
Created at: April 29, 2026, 8:08 p.m.