Triple

T30565290

Position	Surface form	Disambiguated ID	Type / Status
Subject	Imperial Atlantic	`E777955`	entity
Predicate	hasPrimaryCharacterAssociated	`P97041`	FINISHED
Object	Cassie Bowden Cassie Bowden is the hard-drinking, impulsive flight attendant protagonist of the thriller-comedy series "The Flight Attendant," whose life unravels after she becomes entangled in a mysterious death.	`E600960`	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: Cassie Bowden | Statement: [Imperial Atlantic, hasPrimaryCharacterAssociated, Cassie Bowden]

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: Cassie Bowden
Triple: [Imperial Atlantic, hasPrimaryCharacterAssociated, Cassie Bowden]

Generated description

Cassie Bowden is the hard-drinking, impulsive flight attendant protagonist of the thriller-comedy series "The Flight Attendant," whose life unravels after she becomes entangled in a mysterious death.

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

Target predicate: hasPrimaryCharacterAssociated
Context triple: [Imperial Atlantic, hasPrimaryCharacterAssociated, Cassie Bowden]

A. hasPrimaryCharacter chosen
Indicates that an entity features another entity as its main or central character.
B. hasCharacterAssociated
Indicates that one entity is linked to or connected with a particular character (such as a person, figure, or persona) in some relevant way.
C. belongsToCharacter
Indicates that something is owned, associated with, or under the domain of a particular character.
D. hasPrimaryAssociation
Indicates that one entity is chiefly or most directly connected, linked, or related to another among possible associations.
E. hasPrimary
Indicates that one entity is designated as the main or most important instance (the primary) in relation to another entity.
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_69f2249ed41c8190b175170ecfd6e1c5`	completed	April 29, 2026, 3:32 p.m.
NER	Named-entity recognition	`batch_69fff86e544c81908063f61b876c9d78`	completed	May 10, 2026, 3:15 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a2a1e0972d48190813be5609ab3c272`	completed	June 11, 2026, 2:31 a.m.
NEDg	Description generation	`batch_6a2a2823d8408190b62a5e80e6878daf`	completed	June 11, 2026, 3:14 a.m.
NED2	Entity disambiguation (via description)	`batch_6a2a288b41bc8190bfdc652f18191347`	completed	June 11, 2026, 3:16 a.m.
PD	Predicate disambiguation	`batch_69fff7e7cb688190977eeca41aad25b9`	completed	May 10, 2026, 3:13 a.m.

Created at: April 29, 2026, 8:21 p.m.