Triple
T7749022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Pisa |
E175706
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Bientina
Bientina is a small Tuscan town in central Italy known for its historic center and location within the Province of Pisa.
|
E686263
|
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: Bientina | Statement: [Province of Pisa, containsTown, Bientina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bientina Context triple: [Province of Pisa, containsTown, Bientina]
-
A.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
B.
Irati
Irati is a river in northern Spain known for flowing through the Pyrenean landscapes of Navarre and its surrounding beech–fir forests.
-
C.
Rio Claro
Rio Claro is a town in southeastern Trinidad known as a commercial and transportation hub for the surrounding rural communities.
-
D.
Rio Claro
Rio Claro is a municipality in the interior of Brazil’s state of São Paulo, known for its industrial activity and regional educational institutions.
-
E.
Lomati
Lomati is a small village located on Kadavu Island in Fiji, known for its traditional Fijian rural lifestyle and coastal setting.
- 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: Bientina Triple: [Province of Pisa, containsTown, Bientina]
Generated description
Bientina is a small Tuscan town in central Italy known for its historic center and location within the Province of Pisa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bientina Target entity description: Bientina is a small Tuscan town in central Italy known for its historic center and location within the Province of Pisa.
-
A.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
B.
Irati
Irati is a river in northern Spain known for flowing through the Pyrenean landscapes of Navarre and its surrounding beech–fir forests.
-
C.
Rio Claro
Rio Claro is a town in southeastern Trinidad known as a commercial and transportation hub for the surrounding rural communities.
-
D.
Rio Claro
Rio Claro is a municipality in the interior of Brazil’s state of São Paulo, known for its industrial activity and regional educational institutions.
-
E.
Lomati
Lomati is a small village located on Kadavu Island in Fiji, known for its traditional Fijian rural lifestyle and coastal setting.
- 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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703affb6c8190adf4723dc1139edf |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be53b3788190a850ce1aaaac3aaa |
completed | March 29, 2026, 5:53 a.m. |
| NEDg | Description generation | batch_69c8c235b1748190b6c17c5975e2eb9b |
completed | March 29, 2026, 6:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c2f1dd508190853065d9e4e331b2 |
completed | March 29, 2026, 6:13 a.m. |
Created at: March 27, 2026, 4:08 p.m.