Triple
T28177056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carnolès district |
E715919
|
entity |
| Predicate | nearbyPrincipality |
P135404
|
FINISHED |
| Object | Monaco |
—
|
NE NERFINISHED |
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: Monaco | Statement: [Carnolès district, nearbyPrincipality, Monaco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyPrincipality Context triple: [Carnolès district, nearbyPrincipality, Monaco]
-
A.
regionCapitalNearby
Indicates that a capital city of a region is located close to the referenced place or entity.
-
B.
nearbySettlementRegion
Indicates that a settlement is located close to or within the surrounding area of a specified region.
-
C.
locatedNearCountry
chosen
Indicates that one entity is geographically situated close to the borders or territory of a specified country.
-
D.
nearbyCastle
Indicates that one entity is located close to or within a short distance of a castle.
-
E.
nearbyPoliticalEntity
Indicates that one political entity is geographically close to another political entity, without necessarily sharing a border.
- 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_69efd6b4fc5c81909dd88f01a8c2b35d |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: April 27, 2026, 10:17 p.m.