Triple
T11060070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | QGIS |
E261485
|
entity |
| Predicate | supportsFileFormat |
P8463
|
FINISHED |
| Object |
GeoTIFF
GeoTIFF is a geospatial image file format that embeds geographic metadata (such as coordinate system and projection) directly into standard TIFF raster data for use in GIS applications.
|
E902846
|
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: GeoTIFF | Statement: [QGIS, supportsFileFormat, GeoTIFF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GeoTIFF Context triple: [QGIS, supportsFileFormat, GeoTIFF]
-
A.
TIF
TIF is a major annual international trade fair held in Thessaloniki, Greece, showcasing products, services, and innovations from domestic and global exhibitors.
-
B.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
C.
TIF
TIF is the IATA airport code for Taif Regional Airport, which serves the city of Taif in Saudi Arabia.
-
D.
GDAL
GDAL is an open-source geospatial data abstraction library widely used for reading, writing, and transforming a broad range of raster and vector geographic data formats.
-
E.
TIFF
TIFF is the non-profit cultural organization that runs the Toronto International Film Festival and related year-round film programs and events.
- 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: GeoTIFF Triple: [QGIS, supportsFileFormat, GeoTIFF]
Generated description
GeoTIFF is a geospatial image file format that embeds geographic metadata (such as coordinate system and projection) directly into standard TIFF raster data for use in GIS applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: GeoTIFF Target entity description: GeoTIFF is a geospatial image file format that embeds geographic metadata (such as coordinate system and projection) directly into standard TIFF raster data for use in GIS applications.
-
A.
TIF
TIF is a major annual international trade fair held in Thessaloniki, Greece, showcasing products, services, and innovations from domestic and global exhibitors.
-
B.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
C.
TIF
TIF is the IATA airport code for Taif Regional Airport, which serves the city of Taif in Saudi Arabia.
-
D.
GDAL
GDAL is an open-source geospatial data abstraction library widely used for reading, writing, and transforming a broad range of raster and vector geographic data formats.
-
E.
TIFF
TIFF is the non-profit cultural organization that runs the Toronto International Film Festival and related year-round film programs and events.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798e991848190b07c2f48dae38681 |
completed | April 9, 2026, 12:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c87ab0308190a6a6ada1708f0ec2 |
completed | April 18, 2026, 6:07 p.m. |
| NEDg | Description generation | batch_69e3cefc00148190a1850dc6e31523c3 |
completed | April 18, 2026, 6:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3d014a644819092c76aa02b573ca9 |
completed | April 18, 2026, 6:40 p.m. |
Created at: April 8, 2026, 9:26 p.m.