Triple

T30672415
Position Surface form Disambiguated ID Type / Status
Subject Spirits of the Air, Gremlins of the Clouds E780825 entity
Predicate costumeDesignBy P184 FINISHED
Object Michelle Miall
Michelle Miall is a costume designer known for her work on the Australian fantasy film "Spirits of the Air, Gremlins of the Clouds."
E1988946 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: Michelle Miall | Statement: [Spirits of the Air, Gremlins of the Clouds, costumeDesignBy, Michelle Miall]
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: Michelle Miall
Triple: [Spirits of the Air, Gremlins of the Clouds, costumeDesignBy, Michelle Miall]
Generated description
Michelle Miall is a costume designer known for her work on the Australian fantasy film "Spirits of the Air, Gremlins of the Clouds."

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_69f224a7fc208190a07d6d3879b31640 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68b159e3881908e7d125ae27eab41 completed May 2, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4c18c2881908e496565a4354c94 completed June 14, 2026, 4:20 p.m.
NEDg Description generation batch_6a2ed5a7342c8190970ea579d519a5fe completed June 14, 2026, 4:24 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed76555008190a14a24135babd7d9 completed June 14, 2026, 4:31 p.m.
Created at: April 29, 2026, 8:32 p.m.