Triple

T36799517
Position Surface form Disambiguated ID Type / Status
Subject Mayo senior football team E909281 entity
Predicate nickname P55 FINISHED
Object The Green and Red
The Green and Red is the traditional nickname of the Mayo senior Gaelic football team, reflecting the county’s iconic jersey colours and passionate sporting identity.
E2199213 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: The Green and Red | Statement: [Mayo senior football team, nickname, The Green and Red]
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: The Green and Red
Triple: [Mayo senior football team, nickname, The Green and Red]
Generated description
The Green and Red is the traditional nickname of the Mayo senior Gaelic football team, reflecting the county’s iconic jersey colours and passionate sporting identity.

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_69f76e7b98888190899b6478a82ad6ae completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7ca30f4a081909798c24d1768cce5 completed May 3, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3d17aff38c8190ab87df259c2ccd6f completed June 25, 2026, 11:57 a.m.
NEDg Description generation batch_6a3d1b961b2881909a6adfd610a70883 completed June 25, 2026, 12:14 p.m.
NED2 Entity disambiguation (via description) batch_6a3d6891789c81909bafb9134234190f completed June 25, 2026, 5:42 p.m.
Created at: May 3, 2026, 4:12 p.m.