Triple
T17722580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valgrisenche |
E442377
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Bonne
Bonne is a small alpine village in the Valgrisenche valley of Italy’s Aosta Valley region, known for its mountainous surroundings and traditional rural character.
|
E1283381
|
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: Bonne | Statement: [Valgrisenche, hasSettlement, Bonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bonne Context triple: [Valgrisenche, hasSettlement, Bonne]
-
A.
Bonne
Bonne of Berry was a 14th-century French noblewoman of the House of Valois, daughter of John II of France and a politically significant figure through her dynastic marriages.
-
B.
BON
BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
-
C.
BON
BON is the IATA airport code for Flamingo International Airport, the main air gateway to the Caribbean island of Bonaire.
-
D.
Bona
Bona, historically known as Hippo Regius and now Annaba in modern Algeria, is a Mediterranean coastal city that served as a key commercial and strategic port in North Africa.
-
E.
Bona
Bona is a Latin-derived term meaning "good" that appears in various historical and linguistic contexts, including place names and legal or philosophical expressions.
- 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: Bonne Triple: [Valgrisenche, hasSettlement, Bonne]
Generated description
Bonne is a small alpine village in the Valgrisenche valley of Italy’s Aosta Valley region, known for its mountainous surroundings and traditional rural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bonne Target entity description: Bonne is a small alpine village in the Valgrisenche valley of Italy’s Aosta Valley region, known for its mountainous surroundings and traditional rural character.
-
A.
Bonne
Bonne of Berry was a 14th-century French noblewoman of the House of Valois, daughter of John II of France and a politically significant figure through her dynastic marriages.
-
B.
BON
BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
-
C.
BON
BON is the IATA airport code for Flamingo International Airport, the main air gateway to the Caribbean island of Bonaire.
-
D.
Bona
Bona is a Latin-derived term meaning "good" that appears in various historical and linguistic contexts, including place names and legal or philosophical expressions.
-
E.
Bona
Bona, historically known as Hippo Regius and now Annaba in modern Algeria, is a Mediterranean coastal city that served as a key commercial and strategic port in North Africa.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47487b4988190b14237a4e6376e9a |
completed | April 19, 2026, 6:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0230300f548190b9e3bdb6570446ff |
completed | May 11, 2026, 7:38 p.m. |
| NEDg | Description generation | batch_6a02315558cc8190829599a78de8eee2 |
completed | May 11, 2026, 7:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0231e54c308190af6f80f42848536d |
completed | May 11, 2026, 7:45 p.m. |
Created at: April 10, 2026, 10:07 a.m.