Triple

T25949598

Position	Surface form	Disambiguated ID	Type / Status
Subject	Hank Voight	`E653931`	entity
Predicate	familyMember	`P566`	FINISHED
Object	Camille Voight Camille Voight is the late wife of Chicago Police Sergeant Hank Voight in the television series "Chicago P.D.," whose death deeply impacts his personal life and character.	`E1704188`	NE FINISHED

How this triple was built (2 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: Camille Voight | Statement: [Hank Voight, familyMember, Camille Voight]

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: Camille Voight
Triple: [Hank Voight, familyMember, Camille Voight]

Generated description

Camille Voight is the late wife of Chicago Police Sergeant Hank Voight in the television series "Chicago P.D.," whose death deeply impacts his personal life and character.

Provenance (5 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_69e7ab40ac788190a771bc499eb1ae5f`	completed	April 21, 2026, 4:52 p.m.
NER	Named-entity recognition	`batch_69f604938afc8190941e50a9e3911931`	completed	May 2, 2026, 2:05 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a1107762bb881908ff8001b16a1e048`	completed	May 23, 2026, 1:48 a.m.
NEDg	Description generation	`batch_6a11086503f88190b06b786b4cda12d1`	completed	May 23, 2026, 1:52 a.m.
NED2	Entity disambiguation (via description)	`batch_6a1108aa194481908986a597992ffbac`	completed	May 23, 2026, 1:53 a.m.

Created at: April 22, 2026, 8:43 a.m.