Triple
T29910490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 15-inch MacBook Air (M2) |
E759666
|
entity |
| Predicate | displayBrightnessTypical |
P168792
|
FINISHED |
| Object | 500 nits |
—
|
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: 500 nits | Statement: [15-inch MacBook Air (M2), displayBrightnessTypical, 500 nits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: displayBrightnessTypical Context triple: [15-inch MacBook Air (M2), displayBrightnessTypical, 500 nits]
-
A.
surfaceBrightnessClass
Indicates the qualitative classification of how bright an extended object (such as a galaxy) appears per unit area on the sky.
-
B.
surfaceBrightnessProfile
Indicates the distribution of brightness as a function of position across a surface, typically describing how intensity changes from one region to another.
-
C.
hasVariableBrightness
Indicates that the brightness of an entity is not constant but changes over time or under different conditions.
-
D.
maximumBrightness
Indicates the highest level of brightness that an entity can reach or exhibit.
-
E.
reasonForBrightness
Indicates the cause or explanation for why something is bright or has a certain level of brightness.
- 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_69f224600590819085e148a01c056ef6 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f67805551c81909e016ae9e3031076 |
completed | May 2, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
| PDg | Predicate description generation | batch_69f676f73c3481909f01fa69851b7298 |
completed | May 2, 2026, 10:13 p.m. |
Created at: April 29, 2026, 6:10 p.m.