Triple

T28722038
Position Surface form Disambiguated ID Type / Status
Subject Freddy Freeman (Shazam! 2019 film) E730120 entity
Predicate createdForAdaptationBy P21946 FINISHED
Object screenwriter Henry Gayden
Henry Gayden is an American screenwriter best known for writing the superhero film "Shazam!" (2019) and its sequel.
E1831832 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: screenwriter Henry Gayden | Statement: [Freddy Freeman (Shazam! 2019 film), createdForAdaptationBy, screenwriter Henry Gayden]
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: screenwriter Henry Gayden
Triple: [Freddy Freeman (Shazam! 2019 film), createdForAdaptationBy, screenwriter Henry Gayden]
Generated description
Henry Gayden is an American screenwriter best known for writing the superhero film "Shazam!" (2019) and its sequel.

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_69f043e91fe48190b73bcd8e08d433e0 completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f657099ccc8190a2b92a395f5436e7 completed May 2, 2026, 7:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf5ebf2c8190a232e4e23c004d4a completed June 1, 2026, 12:16 a.m.
NEDg Description generation batch_6a249437ba308190b0e40496c8e38562 completed June 6, 2026, 9:42 p.m.
NED2 Entity disambiguation (via description) batch_6a2498ce9614819086c21dc9dc0b45ea completed June 6, 2026, 10:01 p.m.
Created at: April 28, 2026, 5:54 a.m.