Triple
T19439560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caennais |
E486308
|
entity |
| Predicate | hasFeminineCounterpart |
P1613
|
FINISHED |
| Object |
Caennaise
Caennaise is the French feminine demonym referring to a woman or girl from the city of Caen in Normandy, France.
|
E1375055
|
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: Caennaise | Statement: [Caennais, hasFeminineCounterpart, Caennaise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caennaise Context triple: [Caennais, hasFeminineCounterpart, Caennaise]
-
A.
Coedffranc
Coedffranc is a community and electoral ward in Neath Port Talbot, Wales, encompassing several suburban settlements near Swansea.
-
B.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
C.
Fay-de-Bretagne
Fay-de-Bretagne is a rural commune in the Loire-Atlantique department in western France, known for its agricultural landscape and proximity to the town of Blain.
-
D.
Roannais
Roannais is a natural region in central France known for its rolling countryside, agricultural landscapes, and proximity to the upper Loire River.
-
E.
Breton lai
A Breton lai is a short medieval narrative poem, often involving romance, chivalry, and the supernatural, traditionally associated with Brittany and written in verse.
- 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: Caennaise Triple: [Caennais, hasFeminineCounterpart, Caennaise]
Generated description
Caennaise is the French feminine demonym referring to a woman or girl from the city of Caen in Normandy, France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caennaise Target entity description: Caennaise is the French feminine demonym referring to a woman or girl from the city of Caen in Normandy, France.
-
A.
Coedffranc
Coedffranc is a community and electoral ward in Neath Port Talbot, Wales, encompassing several suburban settlements near Swansea.
-
B.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
C.
Fay-de-Bretagne
Fay-de-Bretagne is a rural commune in the Loire-Atlantique department in western France, known for its agricultural landscape and proximity to the town of Blain.
-
D.
Roannais
Roannais is a natural region in central France known for its rolling countryside, agricultural landscapes, and proximity to the upper Loire River.
-
E.
Breton lai
A Breton lai is a short medieval narrative poem, often involving romance, chivalry, and the supernatural, traditionally associated with Brittany and written in verse.
- 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633637ea48190bfa36b0b0a2762bc |
completed | April 20, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0733e22b4c8190b992da00618be661 |
completed | May 15, 2026, 2:55 p.m. |
| NEDg | Description generation | batch_6a07353f35588190b7d6c1f2a34cce3c |
completed | May 15, 2026, 3:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0735c38b7881908489916ff2f2883e |
completed | May 15, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:38 p.m.