Triple
T11545529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United in Stormwind |
E273766
|
entity |
| Predicate | includesCosmetics |
P100026
|
FINISHED |
| Object | card backs |
—
|
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: card backs | Statement: [United in Stormwind, includesCosmetics, card backs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesCosmetics Context triple: [United in Stormwind, includesCosmetics, card backs]
-
A.
hasCosmetics
Indicates that one entity possesses, uses, or is associated with cosmetic products or beauty-related items in relation to another entity or context.
-
B.
cosmeticCategory
Indicates that one entity is classified as belonging to a particular cosmetic or beauty product category defined by the other entity.
-
C.
makeupType
Indicates the specific kind or category of makeup associated with an entity.
-
D.
brandIncludes
Indicates that a broader brand or brand portfolio encompasses, owns, or contains the specified sub-brand, product line, or branded entity.
-
E.
isGenderSpecificCategory
Indicates that the category applies specifically to one gender rather than being gender-neutral.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886e3ad548190b2c88332f5d919bd |
completed | April 10, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69d8087cbe7c819085680f3d67ccc978 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:37 p.m.