Triple
T1934715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blu-ray Disc |
E41417
|
entity |
| Predicate | dataEncodingMethod |
P33682
|
FINISHED |
| Object | pit-and-land optical encoding |
—
|
LITERAL 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: pit-and-land optical encoding | Statement: [Blu-ray Disc, dataEncodingMethod, pit-and-land optical encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataEncodingMethod Context triple: [Blu-ray Disc, dataEncodingMethod, pit-and-land optical encoding]
-
A.
colorEncodingMethod
Indicates the method or scheme used to represent or encode color information.
-
B.
decodingMethod
Indicates the technique or process used to convert encoded or encrypted data back into its original, interpretable form.
-
C.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
D.
colorEncoding
Indicates how the color information of an entity is represented, formatted, or encoded.
-
E.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
- F. None of above. chosen
Provenance (4 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_69abb29b51408190afb2f918814e68c7 |
completed | March 7, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69abaff07cf88190b4883c5f17f90abd |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb25ef0ec8190b907963e9db0fe04 |
completed | March 7, 2026, 5:06 a.m. |
Created at: March 4, 2026, 7:35 p.m.