Triple
T29456631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hold-And-Modify 8 |
E747118
|
entity |
| Predicate | numberOfBitplanes |
P166320
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Hold-And-Modify 8, numberOfBitplanes, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBitplanes Context triple: [Hold-And-Modify 8, numberOfBitplanes, 8]
-
A.
bitplaneCount
chosen
Indicates the number of distinct bitplanes (separate layers of bit-level data) used to represent or encode a value or image.
-
B.
usesBitplanes
Indicates that one entity employs a bitplane-based representation or processing method in relation to another entity or data.
-
C.
extraBitplanesUsedFor
Indicates that additional bitplanes are utilized to provide extra data or capabilities for a specified target (such as an image, layer, or graphical element).
-
D.
typicalBitDepth
Indicates the usual or standard number of bits used to represent each sample or value in a given digital signal or data format.
-
E.
bitWidth
Indicates the number of bits used to represent or encode a given value, type, or data element.
- 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_69f0bd4125f88190b56104591351619c |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69fff4530f908190afe9387f732c2b7e |
completed | May 10, 2026, 2:58 a.m. |
| PD | Predicate disambiguation | batch_69fff3c01a64819091196875b0c88607 |
completed | May 10, 2026, 2:56 a.m. |
Created at: April 28, 2026, 3:46 p.m.