Triple
T11003539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pointer Networks |
E260057
|
entity |
| Predicate | hasFullName |
P16
|
FINISHED |
| Object | Pointer Network |
E260057
|
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: Pointer Network | Statement: [Pointer Networks, hasFullName, Pointer Network]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pointer Network Context triple: [Pointer Networks, hasFullName, Pointer Network]
-
A.
Pointer Networks
chosen
Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
-
B.
GRU
GRU is Russia’s military intelligence agency, known for conducting espionage, cyber operations, and covert activities abroad.
-
C.
GRU
GRU is the IATA airport code for São Paulo–Guarulhos International Airport, the main international gateway serving São Paulo, Brazil.
-
D.
Sequence to Sequence Learning with Neural Networks
"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
-
E.
Pegasos II
Pegasos II is a PowerPC-based computer mainboard developed by Genesi that became popular as a hardware platform for alternative operating systems such as AmigaOS and MorphOS.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3453d181081908cb58a957f4d1295 |
completed | April 18, 2026, 8:47 a.m. |
Created at: April 8, 2026, 9:25 p.m.