Triple
T38635265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Excel 2003 |
E937558
|
entity |
| Predicate | maximumRowsPerWorksheet |
P195573
|
FINISHED |
| Object | 65536 |
—
|
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: 65536 | Statement: [Microsoft Excel 2003, maximumRowsPerWorksheet, 65536]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumRowsPerWorksheet Context triple: [Microsoft Excel 2003, maximumRowsPerWorksheet, 65536]
-
A.
maxNumberOfValues
Indicates the maximum count of distinct values that may be associated with a given entity or property in this relationship.
-
B.
numberOfSheets
Indicates the quantity of individual sheets associated with or contained in an entity.
-
C.
maxFileSize
Indicates the maximum allowable size limit for a file in the given context.
-
D.
fileNameLimit
Indicates a constraint or maximum allowed length or format for a file’s name in a given context.
-
E.
maximumNumberOfUsers
Indicates the highest allowable or supported number of users associated with or participating in a given context or system.
- 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_69f76ed5ca3c81909288f61fbf37b359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdd92396788190ae1424bc1ae55844 |
completed | May 8, 2026, 12:37 p.m. |
| PD | Predicate disambiguation | batch_69fdd678f40481909a717a2daec83b36 |
completed | May 8, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69fdd922d73c81908ad3faade247ec16 |
completed | May 8, 2026, 12:37 p.m. |
Created at: May 3, 2026, 4:32 p.m.