Triple
T22288339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legends Football League |
E550921
|
entity |
| Predicate | uniformCoverage |
P147932
|
FINISHED |
| Object | midriff-exposing tops |
—
|
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: midriff-exposing tops | Statement: [Legends Football League, uniformCoverage, midriff-exposing tops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: uniformCoverage Context triple: [Legends Football League, uniformCoverage, midriff-exposing tops]
-
A.
edgeCoverage
Indicates that one element (such as a test, path, or configuration) covers or exercises a specific edge or connection in a graph or network structure.
-
B.
requiresUniformity
Indicates that one entity imposes a condition that another entity (or set of entities) must be consistent or identical in a specified aspect.
-
C.
uniformizes
Indicates making multiple entities or elements consistent, standardized, or uniform in form, appearance, or behavior.
-
D.
screenCoverage
Indicates the extent to which one entity visually occupies or covers the display area of another (e.g., how much of a screen is taken up by a given element).
-
E.
uniformStyle
Indicates that the related entities share the same or a consistent style, pattern, or formatting.
- 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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15609854c81908adb7681cff2c404 |
completed | April 29, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69e72ffa438481908f80879aef2a589b |
completed | April 21, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e75dc7b08081909d64441e979c2fa4 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 16, 2026, 8:41 p.m.