Triple
T36999607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Lolita |
E915313
|
entity |
| Predicate | dressCodeFor |
P2738
|
FINISHED |
| Object | tea parties and meetups in Lolita communities |
—
|
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: tea parties and meetups in Lolita communities | Statement: [Sweet Lolita, dressCodeFor, tea parties and meetups in Lolita communities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dressCodeFor Context triple: [Sweet Lolita, dressCodeFor, tea parties and meetups in Lolita communities]
-
A.
hasDressCode
chosen
Indicates that a specified entity enforces or is associated with a particular set of rules governing appropriate clothing or attire.
-
B.
dressRecommendation
Indicates a suggested or advised choice of dress for a particular person and/or occasion.
-
C.
usualAttire
Indicates the type of clothing an entity typically wears in ordinary or characteristic situations.
-
D.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or context.
-
E.
designsClothesFor
Indicates that one entity creates or plans clothing specifically intended for 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_69f76e8f1a8c81909db172ed31304971 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb154c0fe08190a2e41e7a29b6055f |
completed | May 6, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69f9fecc005c8190be082a8689193745 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:14 p.m.