Triple
T1316786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ouest Department |
E28121
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Fonds-Verrettes
Fonds-Verrettes is a mountainous commune in southeastern Haiti near the Dominican border, known for its rural character and vulnerability to flooding and landslides.
|
E151583
|
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: Fonds-Verrettes | Statement: [Ouest Department, containsCity, Fonds-Verrettes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fonds-Verrettes Context triple: [Ouest Department, containsCity, Fonds-Verrettes]
-
A.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
B.
Peney-Dessous
Peney-Dessous is a small village in the municipality of Satigny in the canton of Geneva, Switzerland.
-
C.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
D.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
E.
Volnay
Volnay is a renowned wine-producing village in Burgundy, France, celebrated for its elegant, aromatic red wines made primarily from Pinot Noir.
- 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: Fonds-Verrettes Triple: [Ouest Department, containsCity, Fonds-Verrettes]
Generated description
Fonds-Verrettes is a mountainous commune in southeastern Haiti near the Dominican border, known for its rural character and vulnerability to flooding and landslides.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fonds-Verrettes Target entity description: Fonds-Verrettes is a mountainous commune in southeastern Haiti near the Dominican border, known for its rural character and vulnerability to flooding and landslides.
-
A.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
B.
Peney-Dessous
Peney-Dessous is a small village in the municipality of Satigny in the canton of Geneva, Switzerland.
-
C.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
D.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
E.
Volnay
Volnay is a renowned wine-producing village in Burgundy, France, celebrated for its elegant, aromatic red wines made primarily from Pinot Noir.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c175079481909077cf11ed72d6fa |
completed | March 1, 2026, 10:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbf2964048190950723487c7cf707 |
completed | March 8, 2026, 12:13 a.m. |
| NEDg | Description generation | batch_69acbfc03f20819089a025fc745c9203 |
completed | March 8, 2026, 12:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acc0282080819087676813c2852a96 |
completed | March 8, 2026, 12:17 a.m. |
Created at: March 1, 2026, 7:55 p.m.