Triple
T8413320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Human68k |
E198673
|
entity |
| Predicate | textEncodingSupport |
P33284
|
FINISHED |
| Object | Japanese text |
—
|
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: Japanese text | Statement: [Human68k, textEncodingSupport, Japanese text]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textEncodingSupport Context triple: [Human68k, textEncodingSupport, Japanese text]
-
A.
textEncoderType
Indicates the specific kind or configuration of encoder used to process or represent text in a system or model.
-
B.
supportsUnicode
chosen
Indicates that an entity is capable of correctly handling, storing, or displaying Unicode-encoded text.
-
C.
hasUnicode
Indicates that an entity is associated with, represented by, or encoded using a specific Unicode character or sequence.
-
D.
requestEncoding
Indicates that one entity asks another to use or provide a specific encoding format for data or communication.
-
E.
encodedInUnicodeSince
Indicates that a given character or symbol has been included and assigned a code point in the Unicode standard starting from a specific version or time.
- F. None of above.
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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e18e7c8190a30b14136dee3d93 |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:06 p.m.