Triple
T1037489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victor Amadeus II of Savoy |
E22397
|
entity |
| Predicate | positionHeld |
P8
|
FINISHED |
| Object |
Count of Nice
Count of Nice was a noble title within the House of Savoy associated with the rule over the city and surrounding region of Nice.
|
E122157
|
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: Count of Nice | Statement: [Victor Amadeus II of Savoy, positionHeld, Count of Nice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Count of Nice Context triple: [Victor Amadeus II of Savoy, positionHeld, Count of Nice]
-
A.
Count of Saint-Leu
The Count of Saint-Leu was a noble title held by Louis Bonaparte, former King of Holland and younger brother of Napoleon Bonaparte.
-
B.
Casteau
Casteau is a village in Belgium best known as the site of NATO’s Supreme Headquarters Allied Powers Europe (SHAPE).
-
C.
The Lyons
The Lyons are the athletic teams and mascot representing Mount Holyoke College in intercollegiate sports.
-
D.
La Ville Rose
La Ville Rose is the affectionate nickname for the French city of Toulouse, referencing its distinctive pink-hued brick architecture.
-
E.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
- 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: Count of Nice Triple: [Victor Amadeus II of Savoy, positionHeld, Count of Nice]
Generated description
Count of Nice was a noble title within the House of Savoy associated with the rule over the city and surrounding region of Nice.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Count of Nice Target entity description: Count of Nice was a noble title within the House of Savoy associated with the rule over the city and surrounding region of Nice.
-
A.
Count of Saint-Leu
The Count of Saint-Leu was a noble title held by Louis Bonaparte, former King of Holland and younger brother of Napoleon Bonaparte.
-
B.
Casteau
Casteau is a village in Belgium best known as the site of NATO’s Supreme Headquarters Allied Powers Europe (SHAPE).
-
C.
The Lyons
The Lyons are the athletic teams and mascot representing Mount Holyoke College in intercollegiate sports.
-
D.
La Ville Rose
La Ville Rose is the affectionate nickname for the French city of Toulouse, referencing its distinctive pink-hued brick architecture.
-
E.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b82b3ef08190bcd24845b4418d47 |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bc378fc8190846d5ffce73371dd |
completed | March 7, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69ac3df28858819091c594a9cb2aab07 |
completed | March 7, 2026, 3:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac3e5b716c8190b95fde14ee6c434a |
completed | March 7, 2026, 3:03 p.m. |
Created at: March 1, 2026, 7:41 p.m.