Triple
T3435804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Dress D |
E72447
|
entity |
| Predicate | hasUniformCategory |
P23327
|
FINISHED |
| Object | 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: dress uniform | Statement: [Blue Dress D, hasUniformCategory, dress uniform]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUniformCategory Context triple: [Blue Dress D, hasUniformCategory, dress uniform]
-
A.
uniformCategory
chosen
Indicates that two or more entities share the same classification or type within a defined category system.
-
B.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
-
C.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
D.
hasCategoryLevel
Indicates that something is associated with a specific hierarchical category or tier within a classification system.
-
E.
hasCategoryGroup
Indicates that something is associated with, or belongs to, a broader grouping of related categories.
- 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_69ad85af50288190a854b76653deee6f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9f2e4b4819085336fb539daf3c7 |
completed | March 8, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69adae00ad588190bef24373b58a2e1a |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.