Triple
T10104328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Content-Range header field |
E216279
|
entity |
| Predicate | hasSyntaxExample |
P1259
|
FINISHED |
| Object | Content-Range: bytes 0-499/1234 |
—
|
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: Content-Range: bytes 0-499/1234 | Statement: [Content-Range header field, hasSyntaxExample, Content-Range: bytes 0-499/1234]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSyntaxExample Context triple: [Content-Range header field, hasSyntaxExample, Content-Range: bytes 0-499/1234]
-
A.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
C.
hasAwardedForExamples
Indicates that one entity has given an award to another entity specifically in recognition of certain examples or illustrative works.
-
D.
codeExample
Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
-
E.
hasGrammar
Indicates that an entity possesses, follows, or is associated with a particular system of grammatical rules or structure.
- 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_69ca83d039f08190b9d10363221c69fb |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd09c961081909848acec4438c300 |
completed | April 2, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9b853c8190a2af993ce9b21309 |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:03 p.m.