Triple

T33670297

Position	Surface form	Disambiguated ID	Type / Status
Subject	Cole Trickle	`E862598`	entity
Predicate	hasLoveInterestInFilm	`P177260`	FINISHED
Object	Dr. Claire Lewicki Dr. Claire Lewicki is a skilled neurosurgeon and the primary love interest of race car driver Cole Trickle in the film "Days of Thunder."	`E2061602`	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: Dr. Claire Lewicki | Statement: [Cole Trickle, hasLoveInterestInFilm, Dr. Claire Lewicki]

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: Dr. Claire Lewicki
Triple: [Cole Trickle, hasLoveInterestInFilm, Dr. Claire Lewicki]

Generated description

Dr. Claire Lewicki is a skilled neurosurgeon and the primary love interest of race car driver Cole Trickle in the film "Days of Thunder."

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

Target predicate: hasLoveInterestInFilm
Context triple: [Cole Trickle, hasLoveInterestInFilm, Dr. Claire Lewicki]

A. hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
B. hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
C. isRomanticLeadOf chosen
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
D. loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
E. hasRomanticEntanglementInPlot
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
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_69f34984c4008190bb82f33a7819da64`	completed	April 30, 2026, 12:22 p.m.
NER	Named-entity recognition	`batch_69fe91383a1c81909266e40c3c3ede6c`	completed	May 9, 2026, 1:43 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a36272e840c8190b2fc0c0ba5f8ea3b`	completed	June 20, 2026, 5:37 a.m.
NEDg	Description generation	`batch_6a36283aac9c8190836bddb4a59bb063`	completed	June 20, 2026, 5:42 a.m.
NED2	Entity disambiguation (via description)	`batch_6a3628b9d16481908e159baeeedd8c0d`	completed	June 20, 2026, 5:44 a.m.
PD	Predicate disambiguation	`batch_69fe8fde094081908f0f121664fbb5c7`	completed	May 9, 2026, 1:37 a.m.

Created at: May 1, 2026, 1:42 a.m.