Triple

T16431450
Position Surface form Disambiguated ID Type / Status
Subject NSIDC DAAC E399077 entity
Predicate dataFormat P130 FINISHED
Object GeoTIFF E902846 NE 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: GeoTIFF | Statement: [NSIDC DAAC, dataFormat, GeoTIFF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GeoTIFF
Context triple: [NSIDC DAAC, dataFormat, GeoTIFF]
  • A. GeoTIFF chosen
    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.
  • B. TIF
    TIF is a major annual international trade fair held in Thessaloniki, Greece, showcasing products, services, and innovations from domestic and global exhibitors.
  • C. TIF
    TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
  • D. TIF
    TIF is the IATA airport code for Taif Regional Airport, which serves the city of Taif in Saudi Arabia.
  • E. MrSID
    MrSID is a proprietary wavelet-based image compression format commonly used for efficiently storing and distributing large geospatial raster datasets such as aerial and satellite imagery.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d87f2b9024819085c20e52de95d583 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32b9dffe48190a23852f828af55d8 completed April 18, 2026, 6:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f477674819093bcf9f0df43ebf9 completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:10 a.m.