Triple
T8285632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | QEMU |
E193779
|
entity |
| Predicate | supportsDisplayBackend |
P76291
|
FINISHED |
| Object |
EGL
EGL is an interface between Khronos rendering APIs like OpenGL ES and the native windowing system, enabling efficient rendering and context management on a variety of platforms.
|
E724171
|
NE FINISHED |
How this triple was built (4 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: EGL | Statement: [QEMU, supportsDisplayBackend, EGL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EGL Context triple: [QEMU, supportsDisplayBackend, EGL]
-
A.
EGL
EGL is the station code used to identify Eglinton station in transit systems and related services.
-
B.
EGLC
EGLC is the ICAO airport code for London City Airport, a central London hub known for its short runway and business-focused flights.
-
C.
EGLF
EGLF is the ICAO airport code for Farnborough Airport, a major business aviation hub in Hampshire, England.
-
D.
OpenGL ES
OpenGL ES is a cross-platform, royalty-free 2D and 3D graphics API designed for embedded systems such as mobile devices, game consoles, and automotive displays.
-
E.
WGL
WGL is the common abbreviation for the Leibniz Association, a major German network of non-university research institutes spanning a wide range of scientific disciplines.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: EGL Triple: [QEMU, supportsDisplayBackend, EGL]
Generated description
EGL is an interface between Khronos rendering APIs like OpenGL ES and the native windowing system, enabling efficient rendering and context management on a variety of platforms.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EGL Target entity description: EGL is an interface between Khronos rendering APIs like OpenGL ES and the native windowing system, enabling efficient rendering and context management on a variety of platforms.
-
A.
EGL
EGL is the station code used to identify Eglinton station in transit systems and related services.
-
B.
EGLC
EGLC is the ICAO airport code for London City Airport, a central London hub known for its short runway and business-focused flights.
-
C.
EGLF
EGLF is the ICAO airport code for Farnborough Airport, a major business aviation hub in Hampshire, England.
-
D.
OpenGL ES
OpenGL ES is a cross-platform, royalty-free 2D and 3D graphics API designed for embedded systems such as mobile devices, game consoles, and automotive displays.
-
E.
WGL
WGL is the common abbreviation for the Leibniz Association, a major German network of non-university research institutes spanning a wide range of scientific disciplines.
- F. None of above. chosen
Provenance (5 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_69ca82e32db481908b72f3804fa71152 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cbd11ed22c819082bf036602eaa038 |
completed | March 31, 2026, 1:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd688441908190b6b0a39dfb9d87ac |
completed | April 1, 2026, 6:48 p.m. |
| NEDg | Description generation | batch_69cd6d55196881909cf5ec925792e09f |
completed | April 1, 2026, 7:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd7e2bdae08190adc51e904e85695e |
completed | April 1, 2026, 8:21 p.m. |
Created at: March 30, 2026, 5:52 p.m.