Triple
T33443010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sant’Agata de’ Goti |
E856415
|
entity |
| Predicate | hasOldTownName |
P76172
|
FINISHED |
| Object | centro storico di Sant’Agata de’ Goti |
—
|
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: centro storico di Sant’Agata de’ Goti | Statement: [Sant’Agata de’ Goti, hasOldTownName, centro storico di Sant’Agata de’ Goti]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOldTownName Context triple: [Sant’Agata de’ Goti, hasOldTownName, centro storico di Sant’Agata de’ Goti]
-
A.
hasOldCity
Indicates that an entity possesses or contains an old or historic city as part of its domain or structure.
-
B.
hasFormerStreetName
Indicates that an entity (such as a street or place) was previously known by a different street name.
-
C.
nearbyTownFormerName
Indicates that the nearby town previously had a different name, specifying its former name in relation to the current nearby town.
-
D.
historicalTownName
chosen
Indicates that the object is a former or historical name by which the town (subject) was previously known.
-
E.
formerNameOfCapital
Indicates that one entity was the previous official name of a capital city before it was renamed.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e4a4c19c81908acf2da68afec481 |
completed | May 3, 2026, 6:01 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:37 a.m.