Triple
T7523892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | STS-90 |
E177842
|
entity |
| Predicate | partOfProgram |
P2543
|
FINISHED |
| Object | Neurolab program |
E670785
|
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: Neurolab program | Statement: [STS-90, partOfProgram, Neurolab program]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Neurolab program Context triple: [STS-90, partOfProgram, Neurolab program]
-
A.
Neurolab
chosen
Neurolab was a 1998 Space Shuttle STS-90 mission dedicated to studying how microgravity affects the nervous system and brain function in humans and animals.
-
B.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
C.
Cascade-Correlation learning architecture
Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.
-
D.
Hopfield networks
Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
-
E.
“Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
- 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_69c69f29bf3081909a146aec7755f185 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f7c61b508190b582f54ecbb387e3 |
completed | March 27, 2026, 9:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84efbbed48190baa687bb738a0c54 |
completed | March 28, 2026, 9:58 p.m. |
Created at: March 27, 2026, 3:46 p.m.