Triple
T4290666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sant’Andrea al Quirinale |
E97381
|
entity |
| Predicate | hasEntranceOrientation |
P1974
|
FINISHED |
| Object | towards the Quirinal Palace area |
—
|
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: towards the Quirinal Palace area | Statement: [Sant’Andrea al Quirinale, hasEntranceOrientation, towards the Quirinal Palace area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntranceOrientation Context triple: [Sant’Andrea al Quirinale, hasEntranceOrientation, towards the Quirinal Palace area]
-
A.
hasEntrance
Indicates that one entity possesses or provides an entry point or access way to another entity or space.
-
B.
hasEntranceOn
chosen
Indicates that one entity’s entrance or access point is located on or faces a specified side, boundary, or feature of another entity.
-
C.
hasEntranceStructure
Indicates that one entity possesses or is associated with a specific physical structure that serves as its entrance.
-
D.
hasNumberOfEntrances
Indicates the relationship that specifies how many entrances an entity possesses.
-
E.
hasAutomaticEntrance
Indicates that an entity is equipped with an entrance that operates automatically (e.g., opens or closes without manual effort).
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3507efb28819091a9d5b9161a5008 |
completed | March 12, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69b347fc4c0c8190a7fcd814e27308a5 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:08 p.m.