Triple
T10032290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | If-None-Match |
E204878
|
entity |
| Predicate | effectOnPUT |
P92013
|
FINISHED |
| Object | prevents overwriting when resource state already matches given ETag |
—
|
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: prevents overwriting when resource state already matches given ETag | Statement: [If-None-Match, effectOnPUT, prevents overwriting when resource state already matches given ETag]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnPUT Context triple: [If-None-Match, effectOnPUT, prevents overwriting when resource state already matches given ETag]
-
A.
effectOnRepresentation
Indicates how one entity influences, alters, or determines the form, quality, or characteristics of another entity’s representation.
-
B.
effectOnUnion
Indicates the impact or influence that something has on a union as a whole.
-
C.
tookEffect
Indicates that a change, rule, condition, or event became active, operative, or started producing its intended consequences.
-
D.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
E.
effectOnSchedule
Indicates how an event, action, or condition changes, disrupts, or influences a planned schedule or timeline.
- 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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdce461d6481908cc8f968856e0337 |
completed | April 2, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4fed19d481909d2c7ff1114664b6 |
completed | April 1, 2026, 5:03 p.m. |
Created at: March 30, 2026, 8:54 p.m.