Triple

T29471944

Position	Surface form	Disambiguated ID	Type / Status
Subject	Служебный роман	`E747531`	entity
Predicate	главная героиня	`P9202`	FINISHED
Object	Людмила Прокофьевна Калугина Людмила Прокофьевна Калугина — строгая и внешне холодная начальница отдела статистики, которая постепенно раскрывается как ранимая и способная на глубокие чувства женщина в фильме «Служебный роман».	`E1868723`	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: Людмила Прокофьевна Калугина | Statement: [Служебный роман, главная героиня, Людмила Прокофьевна Калугина]

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: Людмила Прокофьевна Калугина
Triple: [Служебный роман, главная героиня, Людмила Прокофьевна Калугина]

Generated description

Людмила Прокофьевна Калугина — строгая и внешне холодная начальница отдела статистики, которая постепенно раскрывается как ранимая и способная на глубокие чувства женщина в фильме «Служебный роман».

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

Target predicate: главная героиня
Context triple: [Служебный роман, главная героиня, Людмила Прокофьевна Калугина]

A. laterMainCharacterOf
Indicates that one entity becomes the main character of a work at a later point in time, succeeding another main character.
B. mainProtagonist chosen
Indicates that the subject is the central character or primary focus in the narrative of the related work.
C. protagonistIs
Indicates that one entity serves as the main character or central figure in relation to another entity or narrative context.
D. protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
E. protagonistField
Indicates that the subject is the main or central character (protagonist) within the specified narrative or context.
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_69f0bd42cf308190bb01b20bc5b7c2d0`	completed	April 28, 2026, 1:59 p.m.
NER	Named-entity recognition	`batch_69f66babf5e08190b8e1007546f3881a`	completed	May 2, 2026, 9:25 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a25f11aae108190b0b52398568cbed6`	completed	June 7, 2026, 10:30 p.m.
NEDg	Description generation	`batch_6a25f566453c8190bfbaf22540ac006e`	completed	June 7, 2026, 10:49 p.m.
NED2	Entity disambiguation (via description)	`batch_6a25f983bf6481909f758942e7a6a26f`	completed	June 7, 2026, 11:06 p.m.
PD	Predicate disambiguation	`batch_69f66339175c819080bd70f0ff7057b1`	completed	May 2, 2026, 8:48 p.m.

Created at: April 28, 2026, 3:57 p.m.