Triple
T37675951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paper I |
E938093
|
entity |
| Predicate | maximumMarks |
P29667
|
FINISHED |
| Object | 100 |
—
|
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: 100 | Statement: [Paper I, maximumMarks, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumMarks Context triple: [Paper I, maximumMarks, 100]
-
A.
passMark
Indicates that an entity has achieved a score or performance level that meets or exceeds the required threshold for passing.
-
B.
hasMaximumGrade
Indicates that an entity possesses the highest possible grade or score within a defined grading or evaluation system.
-
C.
maximumNumber
chosen
Indicates that one entity specifies the highest allowable or observed quantity, value, or count associated with another entity.
-
D.
marks
Indicates that one entity makes a visible or symbolic sign on, or designates, another entity for identification, emphasis, or distinction.
-
E.
marksDetermine
Indicates that one entity’s marks or scores determine or decisively influence the outcome, status, or classification of another entity.
- 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_69f76ed7b1408190ba8c93c53cb8becf |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd884cb2b48190b6acd473430d9e19 |
completed | May 8, 2026, 6:53 a.m. |
| PD | Predicate disambiguation | batch_69fd8709ca208190a8bab836f0156af5 |
completed | May 8, 2026, 6:47 a.m. |
Created at: May 3, 2026, 4:18 p.m.