Triple
T22225848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean I, Count of Armagnac |
E549336
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jean
Jean I, Count of Armagnac, was a 14th-century French nobleman who ruled the County of Armagnac and played a role in the politics of medieval southern France.
|
E1525913
|
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: Jean | Statement: [Jean I, Count of Armagnac, givenName, Jean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Context triple: [Jean I, Count of Armagnac, givenName, Jean]
-
A.
Jean
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Jean
Jean is a fictional mother character from the film "Sweet Sixteen."
-
C.
Jean
Jean is the central protagonist of the crime drama film "I'm Your Woman," a young mother forced into a perilous life on the run after her husband's criminal activities unravel.
-
D.
Jean
Jean is the given name of the actress better known professionally as Vivean Gray, recognized for her roles in British and Australian film and television.
-
E.
Jean
Jean is a fictional character associated with the story of the rhinoceros, likely from Eugène Ionesco’s absurdist play "Rhinocéros."
- 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: Jean Triple: [Jean I, Count of Armagnac, givenName, Jean]
Generated description
Jean I, Count of Armagnac, was a 14th-century French nobleman who ruled the County of Armagnac and played a role in the politics of medieval southern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jean Target entity description: Jean I, Count of Armagnac, was a 14th-century French nobleman who ruled the County of Armagnac and played a role in the politics of medieval southern France.
-
A.
Jean
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Jean
Jean is a given name associated here with Georges Cuvier, the influential French naturalist and zoologist who founded the field of comparative anatomy and helped establish extinction as a scientific fact.
-
C.
Jean
Jean is a common French given name used for both males and females, equivalent to "John" in English.
-
D.
Jean
Jean is the given name of the artist who created the work titled "Allegory of Prudence."
-
E.
Jean
Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
- 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12bee8de8819091ec5d14ea057f9e |
completed | April 28, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0aae66ee5c8190be56910aaecce5d6 |
completed | May 18, 2026, 6:15 a.m. |
| NEDg | Description generation | batch_6a0aaf2f24588190a08dc5158603ee9b |
completed | May 18, 2026, 6:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ab02b3ba48190a8d462a5f68995f2 |
completed | May 18, 2026, 6:22 a.m. |
Created at: April 16, 2026, 8:37 p.m.