Triple
T34800891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 9FIFTY |
E1003214
|
entity |
| Predicate | sizeSystem |
P145679
|
FINISHED |
| Object | one size fits most |
—
|
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: one size fits most | Statement: [9FIFTY, sizeSystem, one size fits most]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sizeSystem Context triple: [9FIFTY, sizeSystem, one size fits most]
-
A.
sizeCategory
Indicates the relative size classification assigned to an entity compared to others (e.g., small, medium, large).
-
B.
sizeStatus
Indicates the relative size condition or classification of one entity in relation to another or to a defined standard.
-
C.
hasSizeSystem
chosen
Indicates that an entity is associated with or defined according to a particular size measurement system.
-
D.
sizeType
Indicates the kind or category of size associated with an entity, such as relative scale, measurement type, or size classification.
-
E.
sizeDescription
Indicates a relationship where one entity provides descriptive information about the size or scale of 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77a89c4e88190a048e95d42b4a084 |
completed | May 3, 2026, 4:40 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.