Triple

T37223140
Position Surface form Disambiguated ID Type / Status
Subject Bosko E922937 entity
Predicate hasRomanticPartner P9994 FINISHED
Object Honey
Honey is an animated character from early Warner Bros. cartoons, known as Bosko’s girlfriend in the Looney Tunes and Merrie Melodies series.
E922938 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: Honey | Statement: [Bosko, hasRomanticPartner, Honey]
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: Honey
Triple: [Bosko, hasRomanticPartner, Honey]
Generated description
Honey is an animated character from early Warner Bros. cartoons, known as Bosko’s girlfriend in the Looney Tunes and Merrie Melodies series.

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_69f76ea7f0008190b31b8e30f3d05a71 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb369e99b48190816e224ae21c83ef completed May 6, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4043baf3d481908b1657e90e461f25 completed June 27, 2026, 9:42 p.m.
NEDg Description generation batch_6a404474c9148190a29659eab3553908 completed June 27, 2026, 9:45 p.m.
NED2 Entity disambiguation (via description) batch_6a4044edf4348190a0441a556da6d4eb completed June 27, 2026, 9:47 p.m.
Created at: May 3, 2026, 4:15 p.m.