Triple

T36145997
Position Surface form Disambiguated ID Type / Status
Subject The Astounding Broccoli Boy E1045450 entity
Predicate mainCharacter P1183 FINISHED
Object Tommy-Lee Komissky
Tommy-Lee Komissky is the young protagonist of Frank Cottrell-Boyce’s children’s novel *The Astounding Broccoli Boy*, who mysteriously turns green and gains unusual abilities.
E2171325 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: Tommy-Lee Komissky | Statement: [The Astounding Broccoli Boy, mainCharacter, Tommy-Lee Komissky]
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: Tommy-Lee Komissky
Triple: [The Astounding Broccoli Boy, mainCharacter, Tommy-Lee Komissky]
Generated description
Tommy-Lee Komissky is the young protagonist of Frank Cottrell-Boyce’s children’s novel *The Astounding Broccoli Boy*, who mysteriously turns green and gains unusual abilities.

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_69f76e37ace88190a906b107d388f5d1 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b35ee8a8819082cddc6b7caedec2 completed May 3, 2026, 8:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a390d48ccd481909608af5cb921da25 completed June 22, 2026, 10:24 a.m.
NEDg Description generation batch_6a390dd6ff808190bdd0b30b261092f5 completed June 22, 2026, 10:26 a.m.
NED2 Entity disambiguation (via description) batch_6a390f2d062481908c4789fba5096e69 completed June 22, 2026, 10:32 a.m.
Created at: May 3, 2026, 4:08 p.m.