Triple
T27952728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Double Negative |
E703470
|
entity |
| Predicate | intendedCondition |
P32577
|
FINISHED |
| Object | subject to natural erosion |
—
|
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: subject to natural erosion | Statement: [Double Negative, intendedCondition, subject to natural erosion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedCondition Context triple: [Double Negative, intendedCondition, subject to natural erosion]
-
A.
targetedCondition
Indicates that an action, intervention, or entity is specifically directed toward affecting, treating, or addressing a particular condition.
-
B.
canCondition
Indicates that one entity has the capability or potential to impose, apply, or bring about a particular condition on another entity or situation.
-
C.
holdsUnderCondition
Indicates that one fact, rule, or relationship remains valid only when a specified condition or set of conditions is satisfied.
-
D.
containsCondition
Indicates that one entity includes, embodies, or is associated with a particular condition.
-
E.
isSupposedToBe
chosen
Indicates that something is expected or intended to have a particular state, quality, or role, whether or not it actually does.
- 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_69ef840c8b2c8190946ae9522774ba51 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f7117e55908190a67105e92bc4830f |
completed | May 3, 2026, 9:12 a.m. |
| PD | Predicate disambiguation | batch_69f70f380690819090cc34763ba460ed |
completed | May 3, 2026, 9:02 a.m. |
Created at: April 27, 2026, 7:25 p.m.