Triple

T27686376
Position Surface form Disambiguated ID Type / Status
Subject Deutsche Film- und Fernsehakademie Berlin E698040 entity
Predicate hasNotableAlumni P51 FINISHED
Object Hans-Christian Schmid
Hans-Christian Schmid is a German film director and screenwriter known for critically acclaimed dramas such as "23," "Requiem," and "Storm."
E2289305 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: Hans-Christian Schmid | Statement: [Deutsche Film- und Fernsehakademie Berlin, hasNotableAlumni, Hans-Christian Schmid]
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: Hans-Christian Schmid
Triple: [Deutsche Film- und Fernsehakademie Berlin, hasNotableAlumni, Hans-Christian Schmid]
Generated description
Hans-Christian Schmid is a German film director and screenwriter known for critically acclaimed dramas such as "23," "Requiem," and "Storm."

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_69ef590df8708190af5488f0638e790c completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f63573baec819099e4d904d908ff02 completed May 2, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5b1de1675081909cca4505080dd1ff completed July 18, 2026, 6:32 a.m.
NEDg Description generation batch_6a5b1e9d8f6081909234273317704bb6 completed July 18, 2026, 6:35 a.m.
NED2 Entity disambiguation (via description) batch_6a5b1f62e3d08190bde35f78fa8c0808 completed July 18, 2026, 6:38 a.m.
Created at: April 27, 2026, 2:49 p.m.