Triple
T36523090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duff's device |
E900227
|
entity |
| Predicate | exploitsLanguageFeature |
P166195
|
FINISHED |
| Object | switch-case fall-through |
—
|
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: switch-case fall-through | Statement: [Duff's device, exploitsLanguageFeature, switch-case fall-through]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exploitsLanguageFeature Context triple: [Duff's device, exploitsLanguageFeature, switch-case fall-through]
-
A.
exploitsFeature
chosen
Indicates that one entity takes advantage of or leverages a particular feature or capability of another entity.
-
B.
languageFeature
Indicates that one entity is a characteristic, property, or capability of a language associated with the other entity.
-
C.
exploits
Indicates that one entity unfairly or selfishly uses another entity or resource for its own advantage or benefit.
-
D.
programmingFeature
Indicates a relationship where one entity is a specific programming-related capability, construct, or functionality provided or supported by another entity (such as a language, tool, or system).
-
E.
scriptUsedForLanguage
Indicates that a particular writing script is employed to write or represent a given language.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.