Triple
T18506480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Likert scale |
E452209
|
entity |
| Predicate | canHaveNumberOfPoints |
P7181
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Likert scale, canHaveNumberOfPoints, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canHaveNumberOfPoints Context triple: [Likert scale, canHaveNumberOfPoints, 4]
-
A.
hasNumberOfPoints
chosen
Indicates that an entity is associated with a specific count of points it possesses or comprises.
-
B.
hasPointsPerLine
Indicates that each line in a given context is associated with a specific number of points.
-
C.
hasLinesThroughEachPoint
Indicates that for every point in the relevant space or set, there exists at least one line that passes through that point.
-
D.
hasComplexPoints
Indicates that something possesses or includes points that are intricate, detailed, or composed of multiple interconnected parts.
-
E.
usesCodeOfPoints
Indicates that one entity evaluates or governs something according to the rules and scoring system defined by another entity’s code of points.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5334266708190b59aca3a2218c095 |
completed | April 19, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69e469dbf5208190b6fc49e02a087f54 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:36 a.m.