Triple

T30925813

Position	Surface form	Disambiguated ID	Type / Status
Subject	Hot Girl (The Office U.S.)	`E787850`	entity
Predicate	guestActorRole	`P80078`	FINISHED
Object	Amy Adams as Katy Amy Adams as Katy refers to the actress’s recurring role as the cheerful purse saleswoman and brief love interest of Jim Halpert in the U.S. version of The Office.	`E1938140`	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: Amy Adams as Katy | Statement: [Hot Girl (The Office U.S.), guestActorRole, Amy Adams as Katy]

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: Amy Adams as Katy
Triple: [Hot Girl (The Office U.S.), guestActorRole, Amy Adams as Katy]

Generated description

Amy Adams as Katy refers to the actress’s recurring role as the cheerful purse saleswoman and brief love interest of Jim Halpert in the U.S. version of The Office.

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_69f224bfaca88190b9d0dfcc86297fe9`	completed	April 29, 2026, 3:33 p.m.
NER	Named-entity recognition	`batch_69f692b8ecd88190a71f001b014efeb4`	completed	May 3, 2026, 12:11 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a28e46dab3081908ba0332d4a0e277a`	completed	June 10, 2026, 4:13 a.m.
NEDg	Description generation	`batch_6a28e67217d0819088c25b78007a986c`	completed	June 10, 2026, 4:22 a.m.
NED2	Entity disambiguation (via description)	`batch_6a28e6d1dd80819088b4c72708425be2`	completed	June 10, 2026, 4:23 a.m.

Created at: April 29, 2026, 8:51 p.m.