Triple
T25310884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I-SID |
E634603
|
entity |
| Predicate | maxNumberOfValues |
P158376
|
FINISHED |
| Object | 16777216 |
—
|
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: 16777216 | Statement: [I-SID, maxNumberOfValues, 16777216]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxNumberOfValues Context triple: [I-SID, maxNumberOfValues, 16777216]
-
A.
maximumNumberOfNCMPs
Indicates the highest allowable or observed count of NCMPs associated with a given context or entity.
-
B.
maximumTermCount
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
C.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
-
D.
maximumNumber
Indicates that one entity specifies the highest allowable or observed quantity, value, or count associated with another entity.
-
E.
maximumEntries
Indicates the relationship that specifies the highest number of entries that are allowed or supported in a given context.
- 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_69e75a972c6481909bc11710e8d30a6c |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f4939e84dc8190bef761d9bfee08a6 |
completed | May 1, 2026, 11:50 a.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
| PDg | Predicate description generation | batch_69f465699c9c8190ac7b4b32b782550c |
completed | May 1, 2026, 8:33 a.m. |
Created at: April 21, 2026, 1:26 p.m.