Triple

T27762499
Position Surface form Disambiguated ID Type / Status
Subject Transformer-XL E701503 entity
Predicate benchmarkedOn P23745 FINISHED
Object One Billion Word Benchmark
The One Billion Word Benchmark is a large-scale English language modeling dataset widely used to evaluate and compare the performance of neural language models.
 E1790161 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: One Billion Word Benchmark | Statement: [Transformer-XL, benchmarkedOn, One Billion Word Benchmark]
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: One Billion Word Benchmark
Triple: [Transformer-XL, benchmarkedOn, One Billion Word Benchmark]
Generated description
The One Billion Word Benchmark is a large-scale English language modeling dataset widely used to evaluate and compare the performance of neural language models.

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_69ef6a5193808190816eb7d0020b2d87 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f63766dcc48190a916106fbf8ad619 completed May 2, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ecb34ae081909234f568f91bd1ae completed May 24, 2026, 12:18 p.m.
NEDg Description generation batch_6a12f0d3217c819082e4210de9b042f1 completed May 24, 2026, 12:36 p.m.
NED2 Entity disambiguation (via description) batch_6a12f202724481909acd0b6d31be8367 completed May 24, 2026, 12:41 p.m.
Created at: April 27, 2026, 4:28 p.m.