Triple
T12403709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bellinzona |
E296324
|
entity |
| Predicate | hasCityWallRemains |
P29117
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bellinzona, hasCityWallRemains, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCityWallRemains Context triple: [Bellinzona, hasCityWallRemains, yes]
-
A.
hasCityWallName
Indicates that a city wall is associated with a specific name or designation.
-
B.
hasHistoricTownWallsRemnants
chosen
Indicates that remnants of historic town walls are present and associated with the subject entity.
-
C.
followsOldCityWall
Indicates that something runs along or traces the path of an old city wall.
-
D.
hasPeaceWalls
Indicates that there exist physical barriers or walls separating groups or areas to reduce or prevent conflict or violence between them.
-
E.
earliestRecordedFortification
Indicates that the subject is the earliest known or first historically recorded example of a particular fortification associated with the object.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.