Triple
T7913960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Megan Griffin |
E183768
|
entity |
| Predicate | usualClothing |
P62728
|
FINISHED |
| Object | pink beanie hat |
—
|
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: pink beanie hat | Statement: [Megan Griffin, usualClothing, pink beanie hat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usualClothing Context triple: [Megan Griffin, usualClothing, pink beanie hat]
-
A.
usualAttire
chosen
Indicates the type of clothing an entity typically wears in ordinary or characteristic situations.
-
B.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
C.
typicallyWornWith
Indicates that one item of clothing or accessory is commonly or customarily worn together with another.
-
D.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
E.
alsoWornIn
Indicates that an item of clothing or accessory is additionally worn in another context, location, or time beyond the primary one mentioned.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a748f4c8190bcd868de2fcf0b3a |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:04 p.m.