Triple
T6121838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumba |
E136502
|
entity |
| Predicate | featuresInversion |
P68257
|
FINISHED |
| Object | vertical loop |
—
|
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: vertical loop | Statement: [Kumba, featuresInversion, vertical loop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresInversion Context triple: [Kumba, featuresInversion, vertical loop]
-
A.
reverseFeature
Indicates that one feature is the inverse or opposite counterpart of another feature in a given context.
-
B.
featuresReimaginedVersionOf
Indicates that something includes or presents a newly interpreted or updated version of another existing work or element.
-
C.
featuresSample
Indicates that an entity includes or presents a particular sample as one of its components or examples.
-
D.
featuresCreator
Indicates that an entity prominently presents, highlights, or showcases a particular creator as a key associated party.
-
E.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
- F. None of above. chosen
Provenance (4 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05bf16ad48190958cc46510e02bd3 |
completed | March 22, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69c049f9ab3c81909c8ab6466f6a2935 |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8e3f2c8190be459ca02f9b315a |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:14 p.m.