Triple
T21514584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oltrarno |
E530809
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Porta Romana
Porta Romana is a historic city gate in Florence, Italy, marking the traditional southern entrance to the city near the Oltrarno district.
|
E1487879
|
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: Porta Romana | Statement: [Oltrarno, contains, Porta Romana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porta Romana Context triple: [Oltrarno, contains, Porta Romana]
-
A.
Porta Romana
Porta Romana is a historic city gate in Velletri, Italy, notable as one of the traditional entrances to the town.
-
B.
Porta Romana
Porta Romana is a historic city gate of Terra del Sole in Italy, notable as one of the main fortified entrances to the Renaissance-planned town.
-
C.
Porta Romana
Porta Romana is a historic city gate and surrounding district in Milan, Italy, known for its architectural heritage and vibrant urban life.
-
D.
Porta Romana
Porta Romana is a historic city gate in Viterbo, Italy, serving as one of the traditional entrances through the town’s medieval walls.
-
E.
Porta Romana
Porta Romana is a historic city gate in Norcia, Italy, notable as one of the main entrances through the town’s medieval walls.
- 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: Porta Romana Triple: [Oltrarno, contains, Porta Romana]
Generated description
Porta Romana is a historic city gate in Florence, Italy, marking the traditional southern entrance to the city near the Oltrarno district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Porta Romana Target entity description: Porta Romana is a historic city gate in Florence, Italy, marking the traditional southern entrance to the city near the Oltrarno district.
-
A.
Porta Romana
Porta Romana is a historic city gate in Viterbo, Italy, serving as one of the traditional entrances through the town’s medieval walls.
-
B.
Porta Romana
Porta Romana is a historic city gate in Norcia, Italy, notable as one of the main entrances through the town’s medieval walls.
-
C.
Porta Romana
Porta Romana is a historic city gate and surrounding district in Milan, Italy, known for its architectural heritage and vibrant urban life.
-
D.
Porta Romana
Porta Romana is a historic city gate in Velletri, Italy, notable as one of the traditional entrances to the town.
-
E.
Porta Romana
Porta Romana is a historic city gate of Terra del Sole in Italy, notable as one of the main fortified entrances to the Renaissance-planned town.
- 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea89b6e48190ac4ea139b895714f |
completed | April 23, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09e146db008190b8d2d7a24a882665 |
completed | May 17, 2026, 3:39 p.m. |
| NEDg | Description generation | batch_6a09e1fc4f7881909e198768cdb43747 |
completed | May 17, 2026, 3:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09e2c86858819092216c12d8aa3348 |
completed | May 17, 2026, 3:46 p.m. |
Created at: April 16, 2026, 6:25 p.m.