Triple
T14011247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saguenay |
E337082
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object |
Laterrière
Laterrière is a former municipality in Quebec, Canada, that was incorporated into the city of Saguenay.
|
E1073010
|
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: Laterrière | Statement: [Saguenay, formedByMergerOf, Laterrière]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laterrière Context triple: [Saguenay, formedByMergerOf, Laterrière]
-
A.
Souvestre
Souvestre is a French surname notably borne by educator Marie Souvestre, known for her progressive influence on women’s education in the 19th century.
-
B.
Raouché
Raouché is a famous seaside neighborhood in western Beirut, Lebanon, best known for its iconic Pigeon Rocks and coastal promenade.
-
C.
duc de Plaisance
The duc de Plaisance was a French ducal title created by Napoleon for statesman Charles-François Lebrun, one of the First French Empire’s leading political figures.
-
D.
Bouvante
Bouvante is a small rural commune in southeastern France, located in the Drôme department within the Auvergne-Rhône-Alpes region.
-
E.
Capucine
Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
- 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: Laterrière Triple: [Saguenay, formedByMergerOf, Laterrière]
Generated description
Laterrière is a former municipality in Quebec, Canada, that was incorporated into the city of Saguenay.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laterrière Target entity description: Laterrière is a former municipality in Quebec, Canada, that was incorporated into the city of Saguenay.
-
A.
Souvestre
Souvestre is a French surname notably borne by educator Marie Souvestre, known for her progressive influence on women’s education in the 19th century.
-
B.
Raouché
Raouché is a famous seaside neighborhood in western Beirut, Lebanon, best known for its iconic Pigeon Rocks and coastal promenade.
-
C.
duc de Plaisance
The duc de Plaisance was a French ducal title created by Napoleon for statesman Charles-François Lebrun, one of the First French Empire’s leading political figures.
-
D.
Bouvante
Bouvante is a small rural commune in southeastern France, located in the Drôme department within the Auvergne-Rhône-Alpes region.
-
E.
Capucine
Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed5cfd0819085b9c860b119a9de |
completed | April 14, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbacaa16e88190995fd86951fb54e6 |
completed | May 6, 2026, 9:03 p.m. |
| NEDg | Description generation | batch_69fbada0a2408190b77d163aee17400e |
completed | May 6, 2026, 9:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbaeeeb594819087b57da166495a72 |
completed | May 6, 2026, 9:13 p.m. |
Created at: April 9, 2026, 10:19 p.m.