Triple
T849001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-2 |
E18339
|
entity |
| Predicate | tokenizerType |
P21075
|
FINISHED |
| Object | Byte Pair 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: Byte Pair Encoding | Statement: [GPT-2, tokenizerType, Byte Pair Encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tokenizerType Context triple: [GPT-2, tokenizerType, Byte Pair Encoding]
-
A.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
B.
termType
Indicates the classification or category of a term within a system, specifying what kind of term it is (e.g., type, role, or function) in relation to others.
-
C.
symbolType
Indicates the classification or category of a symbol based on its role, form, or function within a given system.
-
D.
notationType
Indicates the specific system or style of notation used to represent or encode something (such as music, math, or language).
-
E.
grammaticalType
Indicates the grammatical category or role (such as part of speech or syntactic function) that an expression has within a language.
- 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_69a4938b04208190b82e1df6b572c548 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac1fac3481909cba7070ce31a9b3 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa807adc8190ad808a573cf8e923 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4abb157d08190a7d7281eb3f1b788 |
completed | March 1, 2026, 9:12 p.m. |
Created at: March 1, 2026, 7:38 p.m.