Triple

T35998655

Position	Surface form	Disambiguated ID	Type / Status
Subject	Theme from Angie Tribeca	`E1041064`	entity
Predicate	isThemeForCharacter	`P39449`	FINISHED
Object	Angie Tribeca Angie Tribeca is the deadpan, tough-as-nails LAPD detective at the center of the parody police procedural comedy series "Angie Tribeca."	`E2174612`	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: Angie Tribeca | Statement: [Theme from Angie Tribeca, isThemeForCharacter, Angie Tribeca]

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: Angie Tribeca
Triple: [Theme from Angie Tribeca, isThemeForCharacter, Angie Tribeca]

Generated description

Angie Tribeca is the deadpan, tough-as-nails LAPD detective at the center of the parody police procedural comedy series "Angie Tribeca."

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

Target predicate: isThemeForCharacter
Context triple: [Theme from Angie Tribeca, isThemeForCharacter, Angie Tribeca]

A. themeInvolvingCharacter
Indicates that a theme, motif, or abstract concept centrally involves or is significantly shaped by a particular character.
B. thematicCharacter
Indicates that an entity serves as a central or recurring figure embodying key themes or motifs within a narrative or discourse.
C. characterTheme chosen
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
D. isThemedTo
Indicates that one entity is designed, styled, or conceptually based around the subject, motif, or theme represented by another entity.
E. hasMainThemeCharacter
Indicates that a work (such as a story, film, or game) features a specific character as its central or primary thematic focus.
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_69f76e29084c819083987b828d414de7`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69f7acaec1508190a38f2ac9cc5383e7`	completed	May 3, 2026, 8:14 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a394d1c3efc819087824ebabf0c7ad2`	completed	June 22, 2026, 2:56 p.m.
NEDg	Description generation	`batch_6a3950fd921c8190b5916677a78390b6`	completed	June 22, 2026, 3:13 p.m.
NED2	Entity disambiguation (via description)	`batch_6a39515cda948190a7b87dfe8fbfb48d`	completed	June 22, 2026, 3:14 p.m.
PD	Predicate disambiguation	`batch_69f7ab75387c819091afc3c2128eb903`	completed	May 3, 2026, 8:09 p.m.

Created at: May 3, 2026, 4:07 p.m.