Triple
T31127872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romania at the Winter Olympics |
E793408
|
entity |
| Predicate | hasWonMedalType |
P26560
|
FINISHED |
| Object | bronze |
—
|
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: bronze | Statement: [Romania at the Winter Olympics, hasWonMedalType, bronze]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWonMedalType Context triple: [Romania at the Winter Olympics, hasWonMedalType, bronze]
-
A.
medalTypesAwarded
Indicates the specific types or categories of medals that have been awarded in a given awarding event or context.
-
B.
hasMedalEquivalent
Indicates that one medal is considered equivalent in value, status, or recognition to another medal.
-
C.
hasMedalComponent
Indicates that something includes or is composed of a particular medal or medal-related part as one of its components.
-
D.
hasMedalCount
Indicates the relationship between an entity and the number of medals it possesses or has been awarded.
-
E.
medalType
chosen
Indicates the specific category or class of a medal associated with an award or achievement.
- 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_69f224d1701c819094f429798290e361 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
Created at: April 29, 2026, 9:05 p.m.