Triple

T31974582
Position Surface form Disambiguated ID Type / Status
Subject Masoom E816413 entity
Predicate basedOn P98 FINISHED
Object Man, Woman and Child
"Man, Woman and Child" is a 1980 novel by Erich Segal that explores the emotional upheaval in a seemingly happy family when the husband’s previously unknown illegitimate son enters their lives.
 E1986974 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: Man, Woman and Child | Statement: [Masoom, basedOn, Man, Woman and Child]
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: Man, Woman and Child
Triple: [Masoom, basedOn, Man, Woman and Child]
Generated description
"Man, Woman and Child" is a 1980 novel by Erich Segal that explores the emotional upheaval in a seemingly happy family when the husband’s previously unknown illegitimate son enters their lives.

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_69f348f6a3008190bfb59ca695fd68e2 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b343b8948190993241cef00000dd completed May 3, 2026, 2:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2eb14af92081908816ee3b09003bf3 completed June 14, 2026, 1:48 p.m.
NEDg Description generation batch_6a2eb286d73881909e7a2352a3696226 completed June 14, 2026, 1:54 p.m.
NED2 Entity disambiguation (via description) batch_6a2eb43a83e48190821839ed8957d14c completed June 14, 2026, 2:01 p.m.
Created at: May 1, 2026, 12:11 a.m.