Triple
T1937691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REN |
E41479
|
entity |
| Predicate | tokenType |
P33731
|
FINISHED |
| Object | utility token |
—
|
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: utility token | Statement: [REN, tokenType, utility token]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tokenType Context triple: [REN, tokenType, utility token]
-
A.
tokenizerType
Indicates the specific tokenization method or algorithm used to split text into tokens.
-
B.
keyType
Indicates the classification or category of a key in relation to how it is used or defined within a system or context.
-
C.
elevatedTokenType
Indicates that a token has a higher or special level of privilege, sensitivity, or importance compared to standard tokens.
-
D.
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.
-
E.
slotType
Indicates the classification or category assigned to a particular slot or position within a structure, system, or sequence.
- 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_69abb2c752fc81909baf38ff1cdeb18c |
completed | March 7, 2026, 5:08 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, 7:36 p.m.