Triple
T21121749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campo San Fantin |
E520447
|
entity |
| Predicate | hasNearbyLandmark |
P2064
|
FINISHED |
| Object |
San Fantin
San Fantin is a historic church in central Venice, Italy, known for its Renaissance architecture and proximity to the La Fenice opera house.
|
E1467276
|
NE FINISHED |
How this triple was built (4 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: San Fantin | Statement: [Campo San Fantin, hasNearbyLandmark, San Fantin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Fantin Context triple: [Campo San Fantin, hasNearbyLandmark, San Fantin]
-
A.
San Donaci
San Donaci is a small town and comune in the Apulia region of southern Italy, known for its agricultural economy and production of wine and olive oil.
-
B.
Fontanarrosa
Fontanarrosa is the surname of Roberto Fontanarrosa, a renowned Argentine cartoonist, writer, and humorist.
-
C.
Santa Rosa de Sacco
Santa Rosa de Sacco is a town in central Peru located in the Andean highlands of the Junín region.
-
D.
San Roque
San Roque is a barangay (village-level administrative division) within the city of San Jose in the Philippine province of Negros Oriental.
-
E.
San Roque
San Roque is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and rural agricultural economy.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: San Fantin Triple: [Campo San Fantin, hasNearbyLandmark, San Fantin]
Generated description
San Fantin is a historic church in central Venice, Italy, known for its Renaissance architecture and proximity to the La Fenice opera house.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Fantin Target entity description: San Fantin is a historic church in central Venice, Italy, known for its Renaissance architecture and proximity to the La Fenice opera house.
-
A.
San Donaci
San Donaci is a small town and comune in the Apulia region of southern Italy, known for its agricultural economy and production of wine and olive oil.
-
B.
Fontanarrosa
Fontanarrosa is the surname of Roberto Fontanarrosa, a renowned Argentine cartoonist, writer, and humorist.
-
C.
Santa Rosa de Sacco
Santa Rosa de Sacco is a town in central Peru located in the Andean highlands of the Junín region.
-
D.
San Roque
San Roque is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and rural agricultural economy.
-
E.
San Roque
San Roque is a barangay (village-level administrative division) within the city of San Jose in the Philippine province of Negros Oriental.
- F. None of above. chosen
Provenance (5 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e722348fe08190a1f079b7b0b5dd14 |
completed | April 21, 2026, 7:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0965e9aab88190ae07e2d457950c51 |
completed | May 17, 2026, 6:53 a.m. |
| NEDg | Description generation | batch_6a0966879f08819096d6e9e7abe902c5 |
completed | May 17, 2026, 6:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09670fc80c81909cbce03b5e071428 |
completed | May 17, 2026, 6:58 a.m. |
Created at: April 16, 2026, 2:55 p.m.