Triple

T28080587
Position Surface form Disambiguated ID Type / Status
Subject Green E709663 entity
Predicate isAComponentOf P69124 FINISHED
Object RGB color model
The RGB color model is an additive color system used in digital displays and imaging that represents colors as combinations of red, green, and blue light.
 E1802609 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: RGB color model | Statement: [Green, isAComponentOf, RGB color model]
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: RGB color model
Triple: [Green, isAComponentOf, RGB color model]
Generated description
The RGB color model is an additive color system used in digital displays and imaging that represents colors as combinations of red, green, and blue light.

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_69ef9b7037f0819095bb90eaccbcaf32 completed April 27, 2026, 5:22 p.m.
NER Named-entity recognition batch_69f64044c27081909aec9a41dd197102 completed May 2, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15c9166b2481909791a9198eb90178 completed May 26, 2026, 4:23 p.m.
NEDg Description generation batch_6a15caed4b148190b42af70ea0f6d872 completed May 26, 2026, 4:31 p.m.
NED2 Entity disambiguation (via description) batch_6a15cbdf46208190916381816f411f87 completed May 26, 2026, 4:35 p.m.
Created at: April 27, 2026, 8:51 p.m.