Triple
T33027731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NLST |
E845086
|
entity |
| Predicate | responseCodeExample |
P33257
|
FINISHED |
| Object | 150 File status okay; about to open data connection |
—
|
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: 150 File status okay; about to open data connection | Statement: [NLST, responseCodeExample, 150 File status okay; about to open data connection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: responseCodeExample Context triple: [NLST, responseCodeExample, 150 File status okay; about to open data connection]
-
A.
responseExample
Indicates that one entity serves as an illustrative or sample response corresponding to another entity (such as a prompt, question, or situation).
-
B.
failureResponseCodeExample
Indicates the example HTTP status code that should be returned when the related operation fails.
-
C.
usesResponseCode
chosen
Indicates that one entity produces, returns, or relies on a particular response code as part of its behavior or interaction.
-
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.
backendExample
Indicates that something serves as an example or illustrative instance within a backend or server-side context.
- 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_69f34950749c8190ae05cd27adb16d58 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6f8164698819090c1b471f1caa4c6 |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:23 a.m.