Triple
T10172604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serekunda |
E235366
|
entity |
| Predicate | hasRoadConnectionTo |
P11435
|
FINISHED |
| Object |
Brikama
Brikama is a major town in western Gambia known as an administrative center and hub of education, culture, and trade.
|
E844752
|
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: Brikama | Statement: [Serekunda, hasRoadConnectionTo, Brikama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brikama Context triple: [Serekunda, hasRoadConnectionTo, Brikama]
-
A.
Nossi-Bé
Nossi-Bé is an island off the northwest coast of Madagascar known for its tropical beaches, marine biodiversity, and role as a major tourist destination.
-
B.
Rufisque
Rufisque is a coastal city in western Senegal that developed as an important Atlantic trading port and now forms part of the Dakar metropolitan area.
-
C.
Kidal
Kidal is a remote desert town in northeastern Mali that serves as a key cultural and political center for Tuareg communities in the Adagh region.
-
D.
Duékoué
Duékoué is a town in western Côte d'Ivoire that became notorious as a major site of violence and massacres during the country's civil conflicts.
-
E.
Marudi
Marudi is a small inland town in northern Sarawak, Malaysia, serving as an administrative and commercial hub for the surrounding Baram region.
- 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: Brikama Triple: [Serekunda, hasRoadConnectionTo, Brikama]
Generated description
Brikama is a major town in western Gambia known as an administrative center and hub of education, culture, and trade.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brikama Target entity description: Brikama is a major town in western Gambia known as an administrative center and hub of education, culture, and trade.
-
A.
Nossi-Bé
Nossi-Bé is an island off the northwest coast of Madagascar known for its tropical beaches, marine biodiversity, and role as a major tourist destination.
-
B.
Rufisque
Rufisque is a coastal city in western Senegal that developed as an important Atlantic trading port and now forms part of the Dakar metropolitan area.
-
C.
Kidal
Kidal is a remote desert town in northeastern Mali that serves as a key cultural and political center for Tuareg communities in the Adagh region.
-
D.
Duékoué
Duékoué is a town in western Côte d'Ivoire that became notorious as a major site of violence and massacres during the country's civil conflicts.
-
E.
Marudi
Marudi is a small inland town in northern Sarawak, Malaysia, serving as an administrative and commercial hub for the surrounding Baram region.
- 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_69ca84ceafd0819085828600e11bed6b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdec9f6dd8819081588600499165ee |
completed | April 2, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d3010c386481908bc0c985c0b5ff93 |
completed | April 6, 2026, 12:40 a.m. |
| NEDg | Description generation | batch_69d30256ee40819098569b37eb27d3d1 |
completed | April 6, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d302d248e08190b6b7bae9343b6f9a |
completed | April 6, 2026, 12:48 a.m. |
Created at: March 30, 2026, 9:10 p.m.