Triple

T31824780
Position Surface form Disambiguated ID Type / Status
Subject ABC Kids E812360 entity
Predicate broadcasts P833 FINISHED
Object Fireman Sam
Fireman Sam is a British animated children's television series that follows the adventures of a brave firefighter and his colleagues in the Welsh town of Pontypandy as they respond to emergencies and teach safety lessons.
E1980357 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: Fireman Sam | Statement: [ABC Kids, broadcasts, Fireman Sam]
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: Fireman Sam
Triple: [ABC Kids, broadcasts, Fireman Sam]
Generated description
Fireman Sam is a British animated children's television series that follows the adventures of a brave firefighter and his colleagues in the Welsh town of Pontypandy as they respond to emergencies and teach safety lessons.

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_69f348e97fa48190aa06286962af6dee completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6af82f0688190b7792ccbd0862e30 completed May 3, 2026, 2:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e659ce76c8190b5f830be0f136bfa completed June 14, 2026, 8:26 a.m.
NEDg Description generation batch_6a2e67df9e848190ac29f050a2653988 completed June 14, 2026, 8:35 a.m.
NED2 Entity disambiguation (via description) batch_6a2e6853771481909ebe0e2cdbfa97f5 completed June 14, 2026, 8:37 a.m.
Created at: April 30, 2026, 11:46 p.m.