Triple
T17910944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SARA |
E447816
|
entity |
| Predicate | typeOfAI |
P18468
|
FINISHED |
| Object | shipboard computer |
—
|
LITERAL 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: shipboard computer | Statement: [SARA, typeOfAI, shipboard computer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfAI Context triple: [SARA, typeOfAI, shipboard computer]
-
A.
hasAI
Indicates that one entity possesses, incorporates, or is equipped with an artificial intelligence system.
-
B.
learningRole
Indicates that one entity has a specific role or function within a learning or educational context in relation to another entity.
-
C.
AIPTechnology
Indicates a relationship where an artificial intelligence platform or system is based on, utilizes, or is characterized by a specific technology.
-
D.
typeOfIntelligence
chosen
Indicates that one entity is a specific kind or category of intelligence in relation to another entity.
-
E.
usesNeuralNetworks
Indicates that one entity employs neural network models or techniques as part of its functioning, processing, or decision-making.
- F. None of above.
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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49ea017d081908be850a39edf601f |
completed | April 19, 2026, 9:21 a.m. |
| PD | Predicate disambiguation | batch_69e3d8ec2f6881909d7f54b878cbed37 |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.