Triple

T34967612

Position	Surface form	Disambiguated ID	Type / Status
Subject	Get Low	`E1008444`	entity
Predicate	characterPlayedBySissySpacek	`P200350`	FINISHED
Object	Mattie Darrow Mattie Darrow is a key supporting character in the 2009 drama film "Get Low," serving as a figure from the protagonist’s past whose reappearance helps reveal his long-held secrets and emotional history.	`E2119895`	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: Mattie Darrow | Statement: [Get Low, characterPlayedBySissySpacek, Mattie Darrow]

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: Mattie Darrow
Triple: [Get Low, characterPlayedBySissySpacek, Mattie Darrow]

Generated description

Mattie Darrow is a key supporting character in the 2009 drama film "Get Low," serving as a figure from the protagonist’s past whose reappearance helps reveal his long-held secrets and emotional history.

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

Target predicate: characterPlayedBySissySpacek
Context triple: [Get Low, characterPlayedBySissySpacek, Mattie Darrow]

A. characterPlayedByKathleenQuinlan
Indicates that a given character is portrayed or acted by Kathleen Quinlan.
B. characterPlayedByGinaGershon
Indicates that the subject is a character portrayed by the actress Gina Gershon.
C. characterPlayedBy Larisa Oleynik
Indicates that a specific fictional character is portrayed or acted by Larisa Oleynik.
D. characterPlayedByJudyDavis
Indicates that a given character is portrayed or acted by Judy Davis.
E. characterPlayedBy Jill Clayburgh
Indicates that the specified character is portrayed or acted by Jill Clayburgh.
F. None of above. chosen

Provenance (7 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_69f76dc78a308190a1ac29ad4a9a4895`	completed	May 3, 2026, 3:46 p.m.
NER	Named-entity recognition	`batch_69ff84202eb081908ae21a54a4414d68`	completed	May 9, 2026, 6:59 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a37b26eb49081908a7610f03ca6b975`	completed	June 21, 2026, 9:44 a.m.
NEDg	Description generation	`batch_6a37b348c6d88190ad65c70fcb965538`	completed	June 21, 2026, 9:47 a.m.
NED2	Entity disambiguation (via description)	`batch_6a37b41c5968819082c2da527dea016e`	completed	June 21, 2026, 9:51 a.m.
PD	Predicate disambiguation	`batch_69ff833065e4819098579129d4ee17d3`	completed	May 9, 2026, 6:55 p.m.
PDg	Predicate description generation	`batch_69ff841f2f2081908d72d4f878c538a0`	completed	May 9, 2026, 6:59 p.m.

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