Triple

T37158368

Position	Surface form	Disambiguated ID	Type / Status
Subject	Hill Street police station	`E920573`	entity
Predicate	employsFictionalOfficer	`P31758`	FINISHED
Object	Frank Furillo Frank Furillo is the principled and pragmatic police captain at the center of the television drama "Hill Street Blues."	`E2218565`	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: Frank Furillo | Statement: [Hill Street police station, employsFictionalOfficer, Frank Furillo]

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: Frank Furillo
Triple: [Hill Street police station, employsFictionalOfficer, Frank Furillo]

Generated description

Frank Furillo is the principled and pragmatic police captain at the center of the television drama "Hill Street Blues."

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

Target predicate: employsFictionalOfficer
Context triple: [Hill Street police station, employsFictionalOfficer, Frank Furillo]

A. employsFictionalCharacter
Indicates that one entity (typically an organization or individual) has hired or uses the services of a fictional character in some capacity.
B. hasFictionalSpy
Indicates that an entity includes, features, or is associated with a fictional spy character.
C. isFictionalAgentOf
Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
D. policeCharacter chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
E. hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
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_69f76ea0429081908c711b55599eac3c`	completed	May 3, 2026, 3:49 p.m.
NER	Named-entity recognition	`batch_69fce28d6c3081908bf76f5db63ecf68`	completed	May 7, 2026, 7:05 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a4043ae3c00819082449a5ee69368e0`	completed	June 27, 2026, 9:42 p.m.
NEDg	Description generation	`batch_6a40448854a88190852646c14a8f9864`	completed	June 27, 2026, 9:45 p.m.
NED2	Entity disambiguation (via description)	`batch_6a404505b4048190bfadd456a3214fe1`	completed	June 27, 2026, 9:47 p.m.
PD	Predicate disambiguation	`batch_69fce12d2f08819082134b5eb3db6a24`	completed	May 7, 2026, 6:59 p.m.

Created at: May 3, 2026, 4:15 p.m.