Triple
T4326054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | torchtext |
E96635
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | torchtext.datasets |
E96635
|
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: torchtext.datasets | Statement: [torchtext, hasComponent, torchtext.datasets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: torchtext.datasets Context triple: [torchtext, hasComponent, torchtext.datasets]
-
A.
torchtext (ecosystem)
chosen
torchtext is a PyTorch library that provides tools, datasets, and utilities for building and processing text data in natural language processing workflows.
-
B.
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.
-
C.
AllenNLP
AllenNLP is an open-source natural language processing research library built on PyTorch, designed to facilitate the development and evaluation of state-of-the-art NLP models.
-
D.
CORDE corpus
The CORDE corpus is a large historical Spanish language corpus compiled by the Royal Spanish Academy, used for studying the evolution and usage of Spanish over time.
-
E.
Common Voice dataset
The Common Voice dataset is a large, open-source multilingual speech corpus created by Mozilla to support and democratize voice recognition research and technology.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3513020f481909ff2fec3934f3002 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db98ae888190aac5b5b7839ae7dd |
completed | March 14, 2026, 10:05 p.m. |
Created at: March 12, 2026, 11:13 p.m.