Triple

T30177806
Position Surface form Disambiguated ID Type / Status
Subject Deborah Findlay E767107 entity
Predicate notableRole P22 FINISHED
Object Mary in The Children Act
Mary in *The Children Act* is a supporting character in the legal drama who interacts with the central judge protagonist, helping to illuminate the personal and ethical tensions surrounding a complex court case.
 E1903760 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: Mary in The Children Act | Statement: [Deborah Findlay, notableRole, Mary in The Children Act]
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: Mary in The Children Act
Triple: [Deborah Findlay, notableRole, Mary in The Children Act]
Generated description
Mary in *The Children Act* is a supporting character in the legal drama who interacts with the central judge protagonist, helping to illuminate the personal and ethical tensions surrounding a complex court case.

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_69f2247ba20c81909d34f2bfed706e1e completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f3f6930819088f6bb2c24573ebb completed May 2, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2758648f3481909f2dff4a258bcf73 completed June 9, 2026, 12:03 a.m.
NEDg Description generation batch_6a275ad414b081909ea3fd739d51cdb9 completed June 9, 2026, 12:14 a.m.
NED2 Entity disambiguation (via description) batch_6a275b647ee08190a1590afaccf078b8 completed June 9, 2026, 12:16 a.m.
Created at: April 29, 2026, 7:25 p.m.