Triple

T35624010

Position	Surface form	Disambiguated ID	Type / Status
Subject	Metrocolor	`E1029397`	entity
Predicate	associatedStudioLaboratory	`P95234`	FINISHED
Object	MGM Laboratory MGM Laboratory was the in-house film processing and post-production facility of Metro-Goldwyn-Mayer, known for handling the studio’s color and technical film work.	`E2147147`	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: MGM Laboratory | Statement: [Metrocolor, associatedStudioLaboratory, MGM Laboratory]

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: MGM Laboratory
Triple: [Metrocolor, associatedStudioLaboratory, MGM Laboratory]

Generated description

MGM Laboratory was the in-house film processing and post-production facility of Metro-Goldwyn-Mayer, known for handling the studio’s color and technical film work.

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

Target predicate: associatedStudioLaboratory
Context triple: [Metrocolor, associatedStudioLaboratory, MGM Laboratory]

A. relatedLaboratory chosen
Indicates that one entity has an associated or connected laboratory, such as a facility used for its research, testing, or experimental activities.
B. hasStudio
Indicates that an entity (such as a film, game, or production) is associated with or produced by a particular studio.
C. laboratoryNameInStory
Indicates that a specific laboratory is referred to by a particular name within the context of a story or narrative.
D. aboardLaboratory
Indicates that an entity is physically present on or within a laboratory facility, such as being on board a lab-equipped platform, vehicle, or structure.
E. associatedWithArtistStudio
Indicates that an entity has a relationship or connection to a particular artist’s studio, such as being located in, created in, or otherwise linked to that studio.
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_69f76e0709408190bbe322bf1707ef6b`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_6a037c8d06cc8190ab6a5e18d9d2571e`	completed	May 12, 2026, 7:16 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a385bf555108190b41ad530941c42ee`	completed	June 21, 2026, 9:47 p.m.
NEDg	Description generation	`batch_6a385d4b1968819091585bdbc54148f0`	completed	June 21, 2026, 9:53 p.m.
NED2	Entity disambiguation (via description)	`batch_6a385d8f4d3481909f214de3bbf391cf`	completed	June 21, 2026, 9:54 p.m.
PD	Predicate disambiguation	`batch_6a037a04d8348190a4819666eab42c9b`	completed	May 12, 2026, 7:05 p.m.

Created at: May 3, 2026, 4:05 p.m.