Triple
T31133957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fujifilm X-E3 |
E793585
|
entity |
| Predicate | exposureCompensationRange |
P171185
|
FINISHED |
| Object | ±5 EV |
—
|
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: ±5 EV | Statement: [Fujifilm X-E3, exposureCompensationRange, ±5 EV]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exposureCompensationRange Context triple: [Fujifilm X-E3, exposureCompensationRange, ±5 EV]
-
A.
exposureModes
Indicates the different ways or conditions under which an entity can be exposed to another entity, factor, or influence.
-
B.
focalLengthRange
Indicates the range of focal lengths over which an optical device (such as a lens) can operate or be adjusted.
-
C.
luminanceRange
Indicates the span between the minimum and maximum luminance (brightness) values associated with an entity or visual element.
-
D.
exposureTime
Indicates the duration for which a subject or object is exposed to a particular condition, influence, or medium.
-
E.
exposureLevel
Indicates the degree or intensity to which an entity is subjected or exposed to a particular factor, condition, or influence.
- 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_69f224d1701c819094f429798290e361 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69c6f01e881908fa84f5d429d37ae |
completed | May 3, 2026, 12:53 a.m. |
| PD | Predicate disambiguation | batch_69f69665cd9c819088c388fc82fec42e |
completed | May 3, 2026, 12:27 a.m. |
| PDg | Predicate description generation | batch_69f69c2127088190ae92c72461576d3b |
completed | May 3, 2026, 12:51 a.m. |
Created at: April 29, 2026, 9:05 p.m.