Triple
T33404746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Star Lasso Experience |
E855406
|
entity |
| Predicate | relationToJeanJacket |
P206452
|
FINISHED |
| Object | attempts to train and schedule Jean Jacket |
—
|
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: attempts to train and schedule Jean Jacket | Statement: [Star Lasso Experience, relationToJeanJacket, attempts to train and schedule Jean Jacket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationToJeanJacket Context triple: [Star Lasso Experience, relationToJeanJacket, attempts to train and schedule Jean Jacket]
-
A.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
B.
clothingFeature
Indicates that one entity has a specific clothing-related attribute, detail, or characteristic associated with it.
-
C.
typicallyWornWith
Indicates that one item of clothing or accessory is commonly or customarily worn together with another.
-
D.
wearsSameDesignAs
Indicates that two entities are wearing items that share the same design or pattern.
-
E.
garmentType
Indicates the specific kind or category of garment associated with an entity.
- 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_69f3496e3f1c8190bcecfa82aa9d17ff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f505c88190ac0879ab422c3054 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:36 a.m.