Triple
T11002633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Learning: A Revolutionary Approach to Artificial Intelligence |
E260037
|
entity |
| Predicate | targetReader |
P9917
|
FINISHED |
| Object | students interested in AI |
—
|
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: students interested in AI | Statement: [Deep Learning: A Revolutionary Approach to Artificial Intelligence, targetReader, students interested in AI]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetReader Context triple: [Deep Learning: A Revolutionary Approach to Artificial Intelligence, targetReader, students interested in AI]
-
A.
alternativeReader
Indicates that one entity serves as an alternative or substitute reader for another entity or resource.
-
B.
readsTo
Indicates that one entity reads or recites content aloud for the benefit of another entity.
-
C.
readsFrom
Indicates that one entity obtains or accesses data, information, or content from another entity as a source.
-
D.
readership
chosen
Indicates the relationship in which one party reads, follows, or is the audience for the written or published work of another.
-
E.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797546f448190946ee6442d657dc5 |
completed | April 9, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d72e96be6c8190a46c69f61b2d8cd4 |
completed | April 9, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:25 p.m.