Triple

T32898602

Position	Surface form	Disambiguated ID	Type / Status
Subject	Fredrick Zoller	`E841543`	entity
Predicate	workLocation	`P7`	FINISHED
Object	Paris (film setting) Paris (film setting) is the cinematic depiction of France’s capital city, often portrayed as a backdrop of romance, culture, and wartime intrigue in films such as those featuring the character Fredrick Zoller.	`E2037890`	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: Paris (film setting) | Statement: [Fredrick Zoller, workLocation, Paris (film setting)]

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: Paris (film setting)
Triple: [Fredrick Zoller, workLocation, Paris (film setting)]

Generated description

Paris (film setting) is the cinematic depiction of France’s capital city, often portrayed as a backdrop of romance, culture, and wartime intrigue in films such as those featuring the character Fredrick Zoller.

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_69f34945ae408190b72d8118c83beb77`	completed	April 30, 2026, 12:21 p.m.
NER	Named-entity recognition	`batch_69f6d075d5008190af365f582980738a`	completed	May 3, 2026, 4:35 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3515fb4d30819094de1c5bfaa943a2`	completed	June 19, 2026, 10:12 a.m.
NEDg	Description generation	`batch_6a35174b5dc081909e02c9b204ec086d`	completed	June 19, 2026, 10:17 a.m.
NED2	Entity disambiguation (via description)	`batch_6a35186e68f88190834d45a9cc3643e9`	completed	June 19, 2026, 10:22 a.m.

Created at: May 1, 2026, 1:19 a.m.