Triple
T33433688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TextEncoderStream |
E856193
|
entity |
| Predicate | supportsChunkedProcessing |
P206468
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [TextEncoderStream, supportsChunkedProcessing, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsChunkedProcessing Context triple: [TextEncoderStream, supportsChunkedProcessing, true]
-
A.
supportsBatchProcessing
Indicates that the subject can handle multiple items or tasks in a single grouped operation rather than processing them individually.
-
B.
supportsProgressiveDownload
Indicates that an entity enables or is compatible with progressive download, allowing data to be consumed while it is still being transferred.
-
C.
supportsMultipleStreams
Indicates that the subject is capable of handling or maintaining more than one data or communication stream at the same time.
-
D.
supportsBackpressure
Indicates that the entity can handle and regulate backpressure, allowing it to slow or adjust data flow in response to downstream demand or capacity.
-
E.
supportsDynamicBatching
Indicates that an entity is capable of handling or processing variable-sized batches of items or requests at runtime, rather than requiring a fixed batch size.
- F. None of above. chosen
Provenance (4 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_69f349709e7881908c342b4d34f555f4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f505c88190ac0879ab422c3054 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:36 a.m.