Triple
T10224727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandela Challenge Plate |
E243175
|
entity |
| Predicate | trophyCategory |
P92852
|
FINISHED |
| Object | international rugby union trophy |
—
|
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: international rugby union trophy | Statement: [Mandela Challenge Plate, trophyCategory, international rugby union trophy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trophyCategory Context triple: [Mandela Challenge Plate, trophyCategory, international rugby union trophy]
-
A.
trophyCount
Indicates the number of trophies associated with a given entity.
-
B.
trophyOfficialName
Indicates the official, formally recognized name assigned to a particular trophy.
-
C.
trophy
Indicates that one entity is a trophy awarded or possessed in relation to another entity, typically as a result of winning or achieving something.
-
D.
trophyFeatures
Indicates that a trophy possesses or is characterized by certain features or attributes.
-
E.
trophyColor
Indicates the color attribute assigned to a trophy in the relationship.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69d4d1e9798c8190b437d53d48554ba1 |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d23a9c4c8190abece9e52879c479 |
completed | April 7, 2026, 9:45 a.m. |
Created at: April 6, 2026, 11:16 a.m.