Triple
T12011711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whitney umbrella |
E285918
|
entity |
| Predicate | hasSelfIntersectionAlong |
P72974
|
FINISHED |
| Object | the positive z-axis |
—
|
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: the positive z-axis | Statement: [Whitney umbrella, hasSelfIntersectionAlong, the positive z-axis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSelfIntersectionAlong Context triple: [Whitney umbrella, hasSelfIntersectionAlong, the positive z-axis]
-
A.
hasSelfIntersection
chosen
Indicates that an entity (typically a curve or path) intersects or crosses itself at one or more points.
-
B.
hasCrossingPoint
Indicates that two or more entities intersect or share at least one common point in space or along their paths.
-
C.
hasRightAngleIntersections
Indicates that the entities intersect each other at right (90-degree) angles.
-
D.
hasNotableIntersection
Indicates that two entities intersect or cross at a point that is considered significant or noteworthy in some context.
-
E.
hadCrossingPoints
Indicates that two entities intersected or overlapped at one or more specific points in space or time.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903d7777481908cd5a001f75e2ee3 |
completed | April 10, 2026, 2:06 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.