Triple
T17795778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Livio Borghese |
E444287
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Livio
Livio is an Italian masculine given name of Latin origin, commonly used in Italy and other Romance-language countries.
|
E1288417
|
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: Livio | Statement: [Livio Borghese, givenName, Livio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Livio Context triple: [Livio Borghese, givenName, Livio]
-
A.
Livias
Livias was an ancient town in the region of Perea, east of the Jordan River, known from classical and biblical-era sources.
-
B.
Faetano
Faetano is one of the nine municipalities (castelli) of San Marino, a small enclave known for its historic character and position in the country’s southeastern area.
-
C.
Tullio
Tullio is an Italian given name most famously borne by the mathematician Tullio Levi-Civita, known for his work in tensor calculus and differential geometry.
-
D.
Leo Calini
Leo Calini was an Italian architect known for his work on major public infrastructure projects, including the design of Rome’s central railway hub, Roma Termini.
-
E.
Leonardus
Leonardus is a Latin given name of Germanic origin that underlies later forms such as Lennart and Leonard.
- 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: Livio Triple: [Livio Borghese, givenName, Livio]
Generated description
Livio is an Italian masculine given name of Latin origin, commonly used in Italy and other Romance-language countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Livio Target entity description: Livio is an Italian masculine given name of Latin origin, commonly used in Italy and other Romance-language countries.
-
A.
Livias
Livias was an ancient town in the region of Perea, east of the Jordan River, known from classical and biblical-era sources.
-
B.
Faetano
Faetano is one of the nine municipalities (castelli) of San Marino, a small enclave known for its historic character and position in the country’s southeastern area.
-
C.
Tullio
Tullio is an Italian given name most famously borne by the mathematician Tullio Levi-Civita, known for his work in tensor calculus and differential geometry.
-
D.
Leo Calini
Leo Calini was an Italian architect known for his work on major public infrastructure projects, including the design of Rome’s central railway hub, Roma Termini.
-
E.
Leonardus
Leonardus is a Latin given name of Germanic origin that underlies later forms such as Lennart and Leonard.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487fafc2c8190b28e791267c47e3c |
completed | April 19, 2026, 7:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02f83c9c388190ba48c3644114c4bc |
completed | May 12, 2026, 9:51 a.m. |
| NEDg | Description generation | batch_6a02f9bcaf748190872b29f1427db09a |
completed | May 12, 2026, 9:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02fa679d1c8190bd61fc29a95ac565 |
completed | May 12, 2026, 10:01 a.m. |
Created at: April 10, 2026, 10:13 a.m.