Triple

T24057629
Position Surface form Disambiguated ID Type / Status
Subject Frank Eliscu E595850 entity
Predicate spouse P13 FINISHED
Object Estelle Eliscu
Estelle Eliscu was the wife of American sculptor Frank Eliscu, known for her partnership with him during his artistic career.
E1626023 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: Estelle Eliscu | Statement: [Frank Eliscu, spouse, Estelle Eliscu]
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: Estelle Eliscu
Triple: [Frank Eliscu, spouse, Estelle Eliscu]
Generated description
Estelle Eliscu was the wife of American sculptor Frank Eliscu, known for her partnership with him during his artistic career.

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_69e288c184b081909f1f1751fb8e299a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1da50d6108190a36bffaa475c8b93 completed April 29, 2026, 10:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbcfbd8c88190be8b023849ce9590 completed May 22, 2026, 2:18 a.m.
NEDg Description generation batch_6a0fc0d4bbb48190ad0a2adfd7dc3746 completed May 22, 2026, 2:35 a.m.
NED2 Entity disambiguation (via description) batch_6a0fc17855cc8190b4a353b7e94fa0c3 completed May 22, 2026, 2:37 a.m.
Created at: April 17, 2026, 10:35 p.m.