Triple
T16148474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GRE Computer Science Test |
E391846
|
entity |
| Predicate | questionCount |
P15109
|
FINISHED |
| Object | approximately 66 questions |
—
|
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: approximately 66 questions | Statement: [GRE Computer Science Test, questionCount, approximately 66 questions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: questionCount Context triple: [GRE Computer Science Test, questionCount, approximately 66 questions]
-
A.
numberOfQuestions
chosen
Indicates the total count of questions associated with or contained in a given entity or context.
-
B.
subjectCount
Indicates the number of subjects associated with or involved in a given entity or context.
-
C.
questionPresented
Indicates that a question has been posed or displayed to an entity for consideration or response.
-
D.
numberOfProblems
Indicates the quantity or count of problems associated with a given entity or situation.
-
E.
questionsPerPassage
Indicates the number of questions that are associated with or derived from a single passage.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9551e081908391061b092ff31b |
completed | April 17, 2026, 11:46 a.m. |
| PD | Predicate disambiguation | batch_69e182885bc08190822ae7e8a4b8ac1f |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.