Triple
T5479602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SMS |
E123437
|
entity |
| Predicate | maxCharacterCount |
P32078
|
FINISHED |
| Object | 160 characters in 7-bit 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: 160 characters in 7-bit encoding | Statement: [SMS, maxCharacterCount, 160 characters in 7-bit encoding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxCharacterCount Context triple: [SMS, maxCharacterCount, 160 characters in 7-bit encoding]
-
A.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
C.
maximumCodePoints
Indicates the maximum number of Unicode code points that are allowed or supported in a given context or value.
-
D.
maximumTermCount
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- 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_69bd4648883481909e9775d43300c5fa |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9248ca348190aa116cace0f9b07a |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd91a58c448190904964a439045e05 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:09 p.m.