Triple
T28825530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian Open |
E727889
|
entity |
| Predicate | rankingPointsCategoryMen |
P10762
|
FINISHED |
| Object | Masters 1000 points |
—
|
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: Masters 1000 points | Statement: [Italian Open, rankingPointsCategoryMen, Masters 1000 points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingPointsCategoryMen Context triple: [Italian Open, rankingPointsCategoryMen, Masters 1000 points]
-
A.
wearerRankCategory
Indicates the classification of the wearer's rank into a broader rank category (e.g., junior, senior, officer, etc.) within a ranking system.
-
B.
rankingCategory
chosen
Indicates the classification or type of ranking under which an entity is evaluated or ordered.
-
C.
hasRankingCategory
Indicates that an entity is associated with a particular ranking category or tier within an ordered classification system.
-
D.
fashionCategory
Indicates the classification of an item into a specific fashion-related category or type (e.g., clothing, footwear, accessories).
-
E.
genderOfCategory
Indicates that a given category or class is associated with a particular gender.
- 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_69f0319d09088190bbf14cdf1987792a |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6d0d46aec819091edf97324d793ac |
completed | May 3, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe2183481908ae4e85a59c66f69 |
completed | May 3, 2026, 4:32 a.m. |
Created at: April 28, 2026, 6:35 a.m.