Triple
T18178255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lasagne |
E435217
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | LSTM |
—
|
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: LSTM | Statement: [Lasagne, supports, LSTM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LSTM Context triple: [Lasagne, supports, LSTM]
-
A.
LSTM networks
chosen
LSTM networks are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data by using gated memory cells.
-
B.
GRU
GRU is the abbreviation for the Georgian Rugby Union, the governing body responsible for overseeing and developing rugby union in Georgia.
-
C.
GRU
GRU is the IATA airport code for São Paulo–Guarulhos International Airport, the main international gateway serving São Paulo, Brazil.
-
D.
GRU
GRU is Russia’s military intelligence agency, known for conducting espionage, cyber operations, and covert activities abroad.
-
E.
recurrent neural networks
Recurrent neural networks are a class of artificial neural networks designed to process sequential data by maintaining and updating a hidden state that captures information over time.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5b68f081908aac8210270f1499 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.