Triple
T18204283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DistilBERT |
E435865
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | BERT |
—
|
NE NERFINISHED |
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: BERT | Statement: [DistilBERT, basedOn, BERT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BERT Context triple: [DistilBERT, basedOn, BERT]
-
A.
BERT
chosen
BERT is a widely used transformer-based language model developed by Google that learns deep bidirectional representations of text for tasks like question answering and text classification.
-
B.
bert
Bert is a masculine given name, often used as a short form of names like Albert, Herbert, or Robert, and is borne by various real and fictional characters.
-
C.
DistilBERT
DistilBERT is a smaller, faster, and lighter-weight distilled version of the BERT language model designed to retain most of its performance while being more efficient for practical NLP applications.
-
D.
BertModel
BertModel is a transformer-based neural network architecture from the BERT family used primarily for encoding text representations in natural language processing tasks.
-
E.
RoBERTa
RoBERTa is a robustly optimized transformer-based language model developed by Facebook AI that improves upon BERT through enhanced training strategies and larger-scale data.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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.