Triple
T2314001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python wordmark |
E51020
|
entity |
| Predicate | hasDigitalFormat |
P8017
|
FINISHED |
| Object | JPEG |
E155901
|
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: JPEG | Statement: [Python wordmark, hasDigitalFormat, JPEG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: JPEG Context triple: [Python wordmark, hasDigitalFormat, JPEG]
-
A.
JPEG
chosen
JPEG is a widely used digital image format that compresses photographic content to reduce file size while maintaining acceptable visual quality.
-
B.
PNG
PNG is the three-letter ISO 3166-1 alpha-3 country code representing Papua New Guinea.
-
C.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
D.
TIFF
TIFF is the non-profit cultural organization that runs the Toronto International Film Festival and related year-round film programs and events.
-
E.
Tiff
Tiff is a common shortened form of the given name Tiffany, often used as a casual or affectionate nickname.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc61c1ef08190911d5f58c2e91189 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895f5420819087b403e9772dce9a |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.