Triple

T28894481

Position	Surface form	Disambiguated ID	Type / Status
Subject	Stealing Harvard	`E732801`	entity
Predicate	hasNieceCharacter	`P63741`	FINISHED
Object	Noreen Noreen is a character in the comedy film "Stealing Harvard," portrayed as the niece whose college tuition drives the main plot’s desperate money-raising schemes.	`E1840434`	NE FINISHED

How this triple was built (3 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: Noreen | Statement: [Stealing Harvard, hasNieceCharacter, Noreen]

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: Noreen
Triple: [Stealing Harvard, hasNieceCharacter, Noreen]

Generated description

Noreen is a character in the comedy film "Stealing Harvard," portrayed as the niece whose college tuition drives the main plot’s desperate money-raising schemes.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: hasNieceCharacter
Context triple: [Stealing Harvard, hasNieceCharacter, Noreen]

A. hasNannyCharacter
Indicates that one entity serves as a nanny or caregiver character for another entity.
B. niece chosen
Indicates that one person is the female child of another person's sibling or sibling-in-law.
C. nieceOrNephewOf
Indicates that one person is the niece or nephew (the child of a sibling or sibling-in-law) of another person.
D. hasSisterProtagonists
Indicates that the work features two or more main characters who are sisters as its central protagonists.
E. hasPuppetCharacter
Indicates that one entity features, includes, or is associated with a particular puppet character as part of its content or composition.
F. None of above.

Provenance (6 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_69f05b08c2008190ac426a035a2ed66d`	completed	April 28, 2026, 7 a.m.
NER	Named-entity recognition	`batch_69f65aa2c5fc8190a74ea45c30e714d4`	completed	May 2, 2026, 8:12 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a24d40a19a08190925c245a1de90382`	completed	June 7, 2026, 2:14 a.m.
NEDg	Description generation	`batch_6a24d9370ef48190845aa485c0356b6f`	completed	June 7, 2026, 2:36 a.m.
NED2	Entity disambiguation (via description)	`batch_6a24dd68154481909a11f3fa37288d2c`	completed	June 7, 2026, 2:54 a.m.
PD	Predicate disambiguation	`batch_69f6576487e081908d802f1caf59c423`	completed	May 2, 2026, 7:58 p.m.

Created at: April 28, 2026, 7:58 a.m.