Triple
T3118181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Sluys |
E65112
|
entity |
| Predicate | commander |
P1061
|
FINISHED |
| Object |
Nicolas Béhuchet
Nicolas Béhuchet was a 14th-century French naval commander and royal official who played a leading role in early Hundred Years’ War sea campaigns.
|
E507587
|
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: Nicolas Béhuchet | Statement: [Battle of Sluys, commander, Nicolas Béhuchet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicolas Béhuchet Context triple: [Battle of Sluys, commander, Nicolas Béhuchet]
-
A.
Frédéric Arnault
Frédéric Arnault is a French business executive known for his leadership roles within the LVMH luxury group, particularly in its watch division.
-
B.
Alexandre de Franceschi
Alexandre de Franceschi is a film editor known for his work on the movie "Lion."
-
C.
Charles Beauquier
Charles Beauquier was a French politician and lawyer active in the Third Republic, known for his involvement in centrist republican politics and legislative work.
-
D.
François Dupeyron
François Dupeyron was a French film director and screenwriter known for his humanistic, character-driven dramas.
-
E.
Pierre Lacotte
Pierre Lacotte was a renowned French choreographer and ballet master celebrated for reviving and reconstructing 19th-century Romantic ballets.
- 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: Nicolas Béhuchet Triple: [Battle of Sluys, commander, Nicolas Béhuchet]
Generated description
Nicolas Béhuchet was a 14th-century French naval commander and royal official who played a leading role in early Hundred Years’ War sea campaigns.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nicolas Béhuchet Target entity description: Nicolas Béhuchet was a 14th-century French naval commander and royal official who played a leading role in early Hundred Years’ War sea campaigns.
-
A.
Frédéric Arnault
Frédéric Arnault is a French business executive known for his leadership roles within the LVMH luxury group, particularly in its watch division.
-
B.
Alexandre de Franceschi
Alexandre de Franceschi is a film editor known for his work on the movie "Lion."
-
C.
Charles Beauquier
Charles Beauquier was a French politician and lawyer active in the Third Republic, known for his involvement in centrist republican politics and legislative work.
-
D.
François Dupeyron
François Dupeyron was a French film director and screenwriter known for his humanistic, character-driven dramas.
-
E.
Pierre Lacotte
Pierre Lacotte was a renowned French choreographer and ballet master celebrated for reviving and reconstructing 19th-century Romantic ballets.
- 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_69ad857fcc088190b0c4d45a5cde6f61 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada4e73cc88190846ef37ccf1a0de7 |
completed | March 8, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe3ba3848190bcd62dd21229ca67 |
completed | March 21, 2026, 8:23 p.m. |
| NEDg | Description generation | batch_69bf020b007881908ffc5fd50f89a309 |
completed | March 21, 2026, 8:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf02562e048190b5583f2f959b6c84 |
completed | March 21, 2026, 8:40 p.m. |
Created at: March 8, 2026, 3:04 p.m.