Triple
T3954182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeomen of the Guard |
E84936
|
entity |
| Predicate | hasTypeOfUniform |
P2930
|
FINISHED |
| Object | full dress uniform |
—
|
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: full dress uniform | Statement: [Yeomen of the Guard, hasTypeOfUniform, full dress uniform]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfUniform Context triple: [Yeomen of the Guard, hasTypeOfUniform, full dress uniform]
-
A.
usesUniform
chosen
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
-
B.
hasStandardType
Indicates that something conforms to or is categorized under a defined standard classification or type.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasTypeOfNames
Indicates that one entity possesses or is associated with a particular category or kind of names defined by another entity.
-
E.
hasTypeOfSupport
Indicates that one entity provides or is associated with a particular kind or category of support in relation to another entity.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8ed04e4819096bced8971cd888d |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:30 p.m.