Triple
T24904346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oyster case |
E623662
|
entity |
| Predicate | typicalWaterResistance |
P17203
|
FINISHED |
| Object | at least 100 meters for many models |
—
|
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: at least 100 meters for many models | Statement: [Oyster case, typicalWaterResistance, at least 100 meters for many models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWaterResistance Context triple: [Oyster case, typicalWaterResistance, at least 100 meters for many models]
-
A.
waterResistanceRating
chosen
Indicates the level to which something can resist water penetration or damage under specified conditions.
-
B.
dustResistanceRating
Indicates the level or degree to which something is protected against or unaffected by dust.
-
C.
isDustAndMoistureResistant
Indicates that something is protected against the ingress of dust and moisture to a specified degree.
-
D.
waterTolerance
Indicates the degree to which an entity can withstand or function effectively in the presence of water.
-
E.
hasWaterLevelCharacteristic
Indicates that an entity is associated with a specific property, feature, or behavior related to its water level.
- 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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 18, 2026, 5:27 a.m.