Triple
T10018169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonelli's eagle |
E199548
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Francois Bonelli
François Bonelli was a French ornithologist after whom Bonelli's eagle is named.
|
E834909
|
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: Francois Bonelli | Statement: [Bonelli's eagle, namedAfter, Francois Bonelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francois Bonelli Context triple: [Bonelli's eagle, namedAfter, Francois Bonelli]
-
A.
Pierre-Louis Faloci
Pierre-Louis Faloci is a prominent French architect recognized for his significant contributions to contemporary architecture in France.
-
B.
Jean Leonetti
Jean Leonetti is a French politician and physician known for his work on end-of-life legislation and his long-standing role in center-right national politics.
-
C.
Paolo Bonacelli
Paolo Bonacelli is an Italian actor known for his work in European cinema, including prominent roles in films by directors such as Pier Paolo Pasolini and Dario Argento.
-
D.
Martín Lousteau
Martín Lousteau is an Argentine economist and politician who has served as a national legislator and held key government positions, including Minister of Economy.
-
E.
Aldo Bonnadonna
Aldo Bonnadonna is a fictional character appearing in the television series "Kristin."
- 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: Francois Bonelli Triple: [Bonelli's eagle, namedAfter, Francois Bonelli]
Generated description
François Bonelli was a French ornithologist after whom Bonelli's eagle is named.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Francois Bonelli Target entity description: François Bonelli was a French ornithologist after whom Bonelli's eagle is named.
-
A.
Pierre-Louis Faloci
Pierre-Louis Faloci is a prominent French architect recognized for his significant contributions to contemporary architecture in France.
-
B.
Jean Leonetti
Jean Leonetti is a French politician and physician known for his work on end-of-life legislation and his long-standing role in center-right national politics.
-
C.
Paolo Bonacelli
Paolo Bonacelli is an Italian actor known for his work in European cinema, including prominent roles in films by directors such as Pier Paolo Pasolini and Dario Argento.
-
D.
Martín Lousteau
Martín Lousteau is an Argentine economist and politician who has served as a national legislator and held key government positions, including Minister of Economy.
-
E.
Aldo Bonnadonna
Aldo Bonnadonna is a fictional character appearing in the television series "Kristin."
- 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd4de1588190a89ed575cff0b8c9 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26aa319508190b85b5a5f78603b6a |
completed | April 5, 2026, 1:58 p.m. |
| NEDg | Description generation | batch_69d26b6dfac081908e85d2b1585217b4 |
completed | April 5, 2026, 2:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d26c47d25081908818f18f6b0881b2 |
completed | April 5, 2026, 2:06 p.m. |
Created at: March 30, 2026, 8:53 p.m.