Triple
T18249468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kato Nevrokopi |
E437045
|
entity |
| Predicate | hasMilitaryCemeteries |
P1496
|
FINISHED |
| Object | World War II era |
—
|
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: World War II era | Statement: [Kato Nevrokopi, hasMilitaryCemeteries, World War II era]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryCemeteries Context triple: [Kato Nevrokopi, hasMilitaryCemeteries, World War II era]
-
A.
numberOfCemeteries
Indicates the count of cemeteries associated with a given entity or within a specified area.
-
B.
hasCemetery
chosen
Indicates that one entity possesses, contains, or includes a cemetery associated with it.
-
C.
hasWarGravesMaintainedBy
Indicates that one entity contains war graves whose upkeep and preservation are carried out by another entity.
-
D.
countryOfCemetery
Indicates that a cemetery is located within the territory of a specified country.
-
E.
hasMassGraveOf
Indicates that a location or site contains a mass grave in which the referenced individuals or remains are buried.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd7fa3708190baefd8d938d20807 |
completed | April 19, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e44fcdee748190bae6fb76e0cb22f3 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:33 a.m.