Triple
T30532304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony FE 24mm f/1.4 GM |
E777037
|
entity |
| Predicate | isDustAndMoistureResistant |
P169833
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Sony FE 24mm f/1.4 GM, isDustAndMoistureResistant, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isDustAndMoistureResistant Context triple: [Sony FE 24mm f/1.4 GM, isDustAndMoistureResistant, yes]
-
A.
dustResistanceRating
Indicates the level or degree to which something is protected against or unaffected by dust.
-
B.
isMoistureSensitive
Indicates that the entity is susceptible to damage, degradation, or altered performance when exposed to moisture or humidity.
-
C.
designedToWithstand
Indicates that something has been intentionally created or engineered to resist, endure, or remain functional under specified conditions, forces, or stresses.
-
D.
waterResistanceRating
Indicates the level to which something can resist water penetration or damage under specified conditions.
-
E.
isCorrosionResistantInAir
Indicates that an entity maintains its integrity and does not significantly corrode when exposed to air under specified conditions.
- 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_69f2249c11508190ae7e955755ccfb01 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6884deb64819083f346e9ac4824ba |
completed | May 2, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69f67e42d6688190b60e91d2c388c555 |
completed | May 2, 2026, 10:44 p.m. |
| PDg | Predicate description generation | batch_69f6827a7b9c8190ab13605aacc81df9 |
completed | May 2, 2026, 11:02 p.m. |
Created at: April 29, 2026, 8:18 p.m.