Triple
T35157949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cisternino |
E1015179
|
entity |
| Predicate | hasOldTownQuarter |
P295
|
FINISHED |
| Object |
Bère Vècchie
Bère Vècchie is a historic old-town quarter in the Apulian hilltop town of Cisternino, Italy, known for its narrow alleys and traditional whitewashed architecture.
|
E2128254
|
NE FINISHED |
How this triple was built (3 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: Bère Vècchie | Statement: [Cisternino, hasOldTownQuarter, Bère Vècchie]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bère Vècchie Triple: [Cisternino, hasOldTownQuarter, Bère Vècchie]
Generated description
Bère Vècchie is a historic old-town quarter in the Apulian hilltop town of Cisternino, Italy, known for its narrow alleys and traditional whitewashed architecture.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOldTownQuarter Context triple: [Cisternino, hasOldTownQuarter, Bère Vècchie]
-
A.
hasHistoricTownSquare
Indicates that an entity possesses or includes a town square that is of historical significance.
-
B.
isInOldTownArea
Indicates that an entity is located within the designated old town area of a place.
-
C.
isFromCityWithHistoricArchitecture
Indicates that an entity originates from a city known for its historically significant or architecturally notable buildings and structures.
-
D.
hasHistoricDistrict
chosen
Indicates that an entity possesses or contains a designated historic district within its boundaries or domain.
-
E.
hasHistoricCenterNearby
Indicates that one entity is located close to another entity that serves as a historic center or historically significant core area.
- F. None of above.
Provenance (6 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_69f76ddb3a708190b521ba2970b17178 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78cf39b9c81909268933e60276acf |
completed | May 3, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a37d96864cc81909ba8531d98805f58 |
completed | June 21, 2026, 12:30 p.m. |
| NEDg | Description generation | batch_6a37dc5190d481908a8128de9d4f5ee5 |
completed | June 21, 2026, 12:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a37dd1e759081909e2244af9ee715e6 |
completed | June 21, 2026, 12:46 p.m. |
| PD | Predicate disambiguation | batch_69f78b9106008190930b3b3675b737d6 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:02 p.m.