Triple

T34788540
Position Surface form Disambiguated ID Type / Status
Subject Franz Xaver Kroetz E1002878 entity
Predicate spouse P13 FINISHED
Object Marie Theres Kroetz-Relin
Marie Theres Kroetz-Relin is a German actress and writer known for her work in film and television as well as for her marriage to playwright and director Franz Xaver Kroetz.
E2123943 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: Marie Theres Kroetz-Relin | Statement: [Franz Xaver Kroetz, spouse, Marie Theres Kroetz-Relin]
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: Marie Theres Kroetz-Relin
Triple: [Franz Xaver Kroetz, spouse, Marie Theres Kroetz-Relin]
Generated description
Marie Theres Kroetz-Relin is a German actress and writer known for her work in film and television as well as for her marriage to playwright and director Franz Xaver Kroetz.

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_69f76db47d408190a24fc7164439ea2d completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77a615a5881909ba68ce77c1818c4 completed May 3, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37c61ad3a4819085b783692a00b869 completed June 21, 2026, 11:08 a.m.
NEDg Description generation batch_6a37c748faf881908a9b54717f1fa843 completed June 21, 2026, 11:13 a.m.
NED2 Entity disambiguation (via description) batch_6a37c7e9f2e4819081f46285fb314fa4 completed June 21, 2026, 11:15 a.m.
Created at: May 3, 2026, 3:59 p.m.