Triple
T18204408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | T5 |
E435867
|
entity |
| Predicate | trainingData |
P21226
|
FINISHED |
| Object | Colossal Clean Crawled Corpus |
—
|
NE NERFINISHED |
How this triple was built (3 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: Colossal Clean Crawled Corpus | Statement: [T5, trainingData, Colossal Clean Crawled Corpus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colossal Clean Crawled Corpus Context triple: [T5, trainingData, Colossal Clean Crawled Corpus]
-
A.
Common Crawl
Common Crawl is a massive, publicly available web archive that regularly crawls and stores petabytes of web page data for use in research and large-scale data analysis.
-
B.
Collins Corpus
Collins Corpus is a large, computer-readable collection of real-world English texts used by Collins for corpus-based lexicography and language research.
-
C.
Corpus
Corpus is a common shortened name for Corpus Christi College, one of the historic constituent colleges of the University of Cambridge.
-
D.
COSMAS II corpus search system
COSMAS II corpus search system is a large-scale linguistic search platform for German language text corpora, maintained by the Institut für Deutsche Sprache for research and lexicographic analysis.
-
E.
WebText dataset
The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Colossal Clean Crawled Corpus Target entity description: The Colossal Clean Crawled Corpus (C4) is a massive, cleaned web-text dataset widely used to train large language models and other state-of-the-art NLP systems.
-
A.
Common Crawl
Common Crawl is a massive, publicly available web archive that regularly crawls and stores petabytes of web page data for use in research and large-scale data analysis.
-
B.
Collins Corpus
Collins Corpus is a large, computer-readable collection of real-world English texts used by Collins for corpus-based lexicography and language research.
-
C.
Corpus
Corpus is a common shortened name for Corpus Christi College, one of the historic constituent colleges of the University of Cambridge.
-
D.
COSMAS II corpus search system
COSMAS II corpus search system is a large-scale linguistic search platform for German language text corpora, maintained by the Institut für Deutsche Sprache for research and lexicographic analysis.
-
E.
WebText dataset
The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.
- F. None of above. chosen
Provenance (2 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.