Triple

T27431378
Position Surface form Disambiguated ID Type / Status
Subject Brigitte Hales E690647 entity
Predicate notableWork P4 FINISHED
Object Once Upon a Time
Once Upon a Time is a fantasy drama television series that intertwines classic fairy-tale characters and modern-day storytelling in the fictional town of Storybrooke.
 E1687087 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: Once Upon a Time | Statement: [Brigitte Hales, notableWork, Once Upon a Time]
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: Once Upon a Time
Triple: [Brigitte Hales, notableWork, Once Upon a Time]
Generated description
Once Upon a Time is a fantasy drama television series that intertwines classic fairy-tale characters and modern-day storytelling in the fictional town of Storybrooke.

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_69ef52003fb48190b0f1295246182a86 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d59a6e88190a9042398d754c304 completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b250a1308190a143ca7a952063f2 completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b34db36c81908d6f05b3e610013a completed May 24, 2026, 8:14 a.m.
NED2 Entity disambiguation (via description) batch_6a12b42bd380819087489bdeb2dbfab7 completed May 24, 2026, 8:17 a.m.
Created at: April 27, 2026, 12:42 p.m.