Triple
T10168235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noise_NK pattern |
E235260
|
entity |
| Predicate | messagePatternCount |
P42152
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Noise_NK pattern, messagePatternCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: messagePatternCount Context triple: [Noise_NK pattern, messagePatternCount, 2]
-
A.
definesNumberOfPatterns
Indicates that an entity specifies or determines how many distinct patterns are involved or allowed in a given context.
-
B.
numberOfCounts
chosen
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
maskPattern
Indicates a relationship where one entity serves as a masking template or pattern that determines which parts or aspects of another entity are revealed, hidden, or transformed.
-
D.
payloadCount
Indicates the number of payload items associated with or carried by a given entity or operation.
-
E.
numberOfMarkers
Indicates the quantity or count of markers associated with a given entity or context.
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec6f64a48190883aefce58a65ca6 |
completed | April 2, 2026, 4:11 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba9956c8190a3e15d091e33149d |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:10 p.m.