Triple
T12206576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Accept-Charset |
E290850
|
entity |
| Predicate | ifNoAcceptableCharset |
P103783
|
FINISHED |
| Object | server may respond with 406 Not Acceptable |
—
|
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: server may respond with 406 Not Acceptable | Statement: [Accept-Charset, ifNoAcceptableCharset, server may respond with 406 Not Acceptable]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ifNoAcceptableCharset Context triple: [Accept-Charset, ifNoAcceptableCharset, server may respond with 406 Not Acceptable]
-
A.
usesCharacterSet
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
B.
hasMIMECharsetName
Indicates that an entity (such as a character encoding) is associated with a specific MIME charset name used in internet protocols.
-
C.
differenceFromContentEncoding
Indicates that one encoding or representation of content differs from another specified content encoding.
-
D.
requestEncoding
Indicates that one entity asks another to use or provide a specific encoding format for data or communication.
-
E.
encodingIndependence
Indicates that a relationship or property holds regardless of the specific encoding or representation used for the involved entities.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d920c3dc9881908c396a4ab34f4836 |
completed | April 10, 2026, 4:09 p.m. |
Created at: April 8, 2026, 9:51 p.m.