Triple

T27715868
Position Surface form Disambiguated ID Type / Status
Subject Mark Blum E698813 entity
Predicate spouse P13 FINISHED
Object Janet Zarish
Janet Zarish is an American actress and acting teacher known for her work in television, film, and on stage, as well as for her long association with New York’s theater community.
 E1817027 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: Janet Zarish | Statement: [Mark Blum, spouse, Janet Zarish]
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: Janet Zarish
Triple: [Mark Blum, spouse, Janet Zarish]
Generated description
Janet Zarish is an American actress and acting teacher known for her work in television, film, and on stage, as well as for her long association with New York’s theater community.

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_69ef591012dc8190a6f1ec994f9f7ff7 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f635cfa6088190aae92d408c036238 completed May 2, 2026, 5:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632dc42088190b810f28bed58a14d completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1634c284bc8190a09d836655486dc7 completed May 27, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a1638ab3f5c8190be17ee9121039cd1 completed May 27, 2026, 12:19 a.m.
Created at: April 27, 2026, 3:04 p.m.