Triple
T2682246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law School Admission Test |
E57398
|
entity |
| Predicate | scoreType |
P16725
|
FINISHED |
| Object | scaled score |
—
|
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: scaled score | Statement: [Law School Admission Test, scoreType, scaled score]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scoreType Context triple: [Law School Admission Test, scoreType, scaled score]
-
A.
scoreScale
chosen
Indicates the scale or range on which a score or rating is expressed or measured.
-
B.
scoring
Indicates the act of achieving points or a measurable result, typically by successfully completing an action that contributes to a score or outcome.
-
C.
scoreUsedFor
Indicates that a particular score or rating is used for a specific purpose, decision, or downstream process.
-
D.
rankingType
Indicates the specific basis or method by which items are ordered or ranked relative to one another.
-
E.
isScoreFor
Indicates that one value represents the score or result associated with a particular entity, event, or performance.
- 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_69ab4a5028388190a36f3baf1588309e |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abda2f7bf88190a1e3103dd014d871 |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ab9d08190b72b6104c6dbc769 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.