Triple
T24806937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnson solids |
E620679
|
entity |
| Predicate | areUniform |
P157345
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [Johnson solids, areUniform, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areUniform Context triple: [Johnson solids, areUniform, false]
-
A.
usesUniform
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
-
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.
isUniformVariant
Indicates that one entity is a stylistic or formatting variant of another while preserving the same underlying content or structure.
-
D.
uniformizes
Indicates making multiple entities or elements consistent, standardized, or uniform in form, appearance, or behavior.
-
E.
includesUniformType
Indicates that one entity contains or encompasses a specific type of uniform within its scope or composition.
- 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_69e2fabf26bc8190b191faac8f67065b |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f42d9000b8819081ea2605f3c193d6 |
completed | May 1, 2026, 4:35 a.m. |
| PD | Predicate disambiguation | batch_69f420f471a0819095a6cd24ed8f7476 |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f42b11251881908070b93355de64ad |
completed | May 1, 2026, 4:24 a.m. |
Created at: April 18, 2026, 4:50 a.m.