Triple
T6213799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sant'Agnese in Agone |
E138933
|
entity |
| Predicate | hasTwinTowers |
P68937
|
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: [Sant'Agnese in Agone, hasTwinTowers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTwinTowers Context triple: [Sant'Agnese in Agone, hasTwinTowers, yes]
-
A.
hasTwinCities
Indicates that two cities are officially recognized as twin (or sister) cities, typically based on cultural, economic, or historical partnership agreements.
-
B.
hasArchitecturalTwin
Indicates that two entities share nearly identical architectural design, form, or structure, effectively making them architectural counterparts or duplicates.
-
C.
One World Trade CenterInstanceOf
Indicates that One World Trade Center belongs to or is classified as a specific type or class of thing (its general category).
-
D.
wasTallestBuildingInNewYorkUntil
Indicates that a building held the status of being the tallest building in New York up to a specified point in time.
-
E.
hasTwinCityStructure
Indicates that one city has an officially recognized twin-city (sister-city) relationship structure with another city.
- F. None of above. chosen
Provenance (4 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0629fd3c08190a121097c188417c4 |
completed | March 22, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69c055fdea3c81908f5d910f0d36234a |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:21 p.m.