Triple

T28143141
Position Surface form Disambiguated ID Type / Status
Subject Middle School: The Worst Years of My Life (film) E714403 entity
Predicate screenwriter P2831 FINISHED
Object Kara Holden
Kara Holden is a screenwriter best known for adapting the popular children's novel "Middle School: The Worst Years of My Life" into a feature film.
E1830398 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: Kara Holden | Statement: [Middle School: The Worst Years of My Life (film), screenwriter, Kara Holden]
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: Kara Holden
Triple: [Middle School: The Worst Years of My Life (film), screenwriter, Kara Holden]
Generated description
Kara Holden is a screenwriter best known for adapting the popular children's novel "Middle School: The Worst Years of My Life" into a feature film.

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_69efd6af156c81908f50c2cd7db0e1ef completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f6417088ac81909668030d2a9daebc completed May 2, 2026, 6:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf12592c81909fc7c6127a50ca7d completed June 1, 2026, 12:15 a.m.
NEDg Description generation batch_6a1cd03986848190a322d5273d0164d0 completed June 1, 2026, 12:20 a.m.
NED2 Entity disambiguation (via description) batch_6a249466d5b08190bd3886ef517cb367 completed June 6, 2026, 9:43 p.m.
Created at: April 27, 2026, 9:54 p.m.