Triple

T27729420
Position Surface form Disambiguated ID Type / Status
Subject Java Monster E697389 entity
Predicate hasCompetitor P1375 FINISHED
Object Starbucks Doubleshot Energy
Starbucks Doubleshot Energy is a ready-to-drink coffee-based energy beverage that combines espresso, milk, and added vitamins and minerals to provide both caffeine and caloric energy.
 E1787213 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: Starbucks Doubleshot Energy | Statement: [Java Monster, hasCompetitor, Starbucks Doubleshot Energy]
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: Starbucks Doubleshot Energy
Triple: [Java Monster, hasCompetitor, Starbucks Doubleshot Energy]
Generated description
Starbucks Doubleshot Energy is a ready-to-drink coffee-based energy beverage that combines espresso, milk, and added vitamins and minerals to provide both caffeine and caloric energy.

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_69ef590c3e288190ad54d2465af8ca4e completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6369a22688190a5674191c90a5d12 completed May 2, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e46f9994819083079e17bf0b28fb completed May 24, 2026, 11:43 a.m.
NEDg Description generation batch_6a12e5ee8f048190b98a89d0023e1eba completed May 24, 2026, 11:50 a.m.
NED2 Entity disambiguation (via description) batch_6a12e695c82c81908646433dbbe85bfc completed May 24, 2026, 11:52 a.m.
Created at: April 27, 2026, 3:10 p.m.