Triple
T1936083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Photos |
E41445
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | TIFF |
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: TIFF | Statement: [Photos, supports, TIFF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TIFF Context triple: [Photos, supports, TIFF]
-
A.
TIFF
TIFF is the non-profit cultural organization that runs the Toronto International Film Festival and related year-round film programs and events.
-
B.
TIF
TIF is the former New York Stock Exchange ticker symbol for Tiffany & Co., the luxury jewelry and specialty retailer.
-
C.
JPEG
chosen
JPEG is a widely used digital image format that compresses photographic content to reduce file size while maintaining acceptable visual quality.
-
D.
AVI
AVI (Audio Video Interleave) is a multimedia container format developed by Microsoft for storing synchronized audio and video data.
-
E.
MPEG
MPEG is a family of widely used digital audio and video compression standards developed by the Moving Picture Experts Group for efficient storage and transmission of multimedia content.
- 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2c5f6e481909b2d95861e2098f9 |
completed | March 7, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3f3932081909a72d1022259359e |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.