Triple

T24678237
Position Surface form Disambiguated ID Type / Status
Subject Mary Chase E611050 entity
Predicate hasChild P369 FINISHED
Object Michael Chase
Michael Chase is the child of American playwright Mary Chase, best known for writing the Pulitzer Prize–winning play "Harvey."
E1645343 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: Michael Chase | Statement: [Mary Chase, hasChild, Michael Chase]
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: Michael Chase
Triple: [Mary Chase, hasChild, Michael Chase]
Generated description
Michael Chase is the child of American playwright Mary Chase, best known for writing the Pulitzer Prize–winning play "Harvey."

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fbe0b888190889cf9a529e84aac completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1004ab304881908a4a8617c1bc9f63 completed May 22, 2026, 7:24 a.m.
NEDg Description generation batch_6a10079f208c81908f5683ebb2401950 completed May 22, 2026, 7:37 a.m.
NED2 Entity disambiguation (via description) batch_6a100857ceec81909d4a9169cb7cafe3 completed May 22, 2026, 7:40 a.m.
Created at: April 18, 2026, 3:07 a.m.