Triple
T5671552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1959 Mediterranean Games |
E124988
|
entity |
| Predicate | regionalScope |
P32691
|
FINISHED |
| Object | countries bordering the Mediterranean Sea |
—
|
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: countries bordering the Mediterranean Sea | Statement: [1959 Mediterranean Games, regionalScope, countries bordering the Mediterranean Sea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalScope Context triple: [1959 Mediterranean Games, regionalScope, countries bordering the Mediterranean Sea]
-
A.
region1
Indicates that one entity is the first or primary region associated with, containing, or encompassing another entity.
-
B.
regionContext
chosen
Indicates the broader geographic or spatial setting within which an entity, event, or relationship is situated or interpreted.
-
C.
arealRegion
Indicates that something occupies or pertains to a specific two-dimensional geographic or spatial area.
-
D.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
E.
campaignRegion
Indicates the geographic area or territory in which a campaign is conducted or targeted.
- 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_69c008295c808190acfe78915e7d656a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_69c021bc3894819084f37d14ba4b2644 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:43 p.m.