Triple
T10172583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serekunda |
E235366
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object |
Latri Kunda
Latri Kunda is a neighborhood within the urban area of Serekunda in The Gambia, known for its busy streets and local commerce.
|
E846071
|
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: Latri Kunda | Statement: [Serekunda, hasNeighborhood, Latri Kunda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Latri Kunda Context triple: [Serekunda, hasNeighborhood, Latri Kunda]
-
A.
Kadamtala
Kadamtala is a small settlement located on Middle Andaman Island in the Andaman and Nicobar Islands of India.
-
B.
Haladhara
Haladhara is another name for the Hindu deity Balarama, revered as Krishna’s elder brother and famed for wielding the plough as his primary weapon.
-
C.
Maheshtala
Maheshtala is a suburban city near Kolkata in West Bengal, India, known for its rapidly growing residential areas and emerging industrial and commercial development.
-
D.
Garudadri
Garudadri is one of the sacred hills of the Tirumala range, traditionally associated with the divine mount Garuda in Hindu mythology.
-
E.
Shingora
Shingora is a film featuring Indian actress and model Persis Khambatta, known for her distinctive screen presence and international appeal.
- 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: Latri Kunda Triple: [Serekunda, hasNeighborhood, Latri Kunda]
Generated description
Latri Kunda is a neighborhood within the urban area of Serekunda in The Gambia, known for its busy streets and local commerce.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Latri Kunda Target entity description: Latri Kunda is a neighborhood within the urban area of Serekunda in The Gambia, known for its busy streets and local commerce.
-
A.
Kadamtala
Kadamtala is a small settlement located on Middle Andaman Island in the Andaman and Nicobar Islands of India.
-
B.
Haladhara
Haladhara is another name for the Hindu deity Balarama, revered as Krishna’s elder brother and famed for wielding the plough as his primary weapon.
-
C.
Maheshtala
Maheshtala is a suburban city near Kolkata in West Bengal, India, known for its rapidly growing residential areas and emerging industrial and commercial development.
-
D.
Garudadri
Garudadri is one of the sacred hills of the Tirumala range, traditionally associated with the divine mount Garuda in Hindu mythology.
-
E.
Shingora
Shingora is a film featuring Indian actress and model Persis Khambatta, known for her distinctive screen presence and international appeal.
- 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_69d30101e3ec819095a587c0dae55f71 |
completed | April 6, 2026, 12:40 a.m. |
| NEDg | Description generation | batch_69d30255c7408190a56764f3d3f36ee2 |
completed | April 6, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d30343f4b081909eb80c772f6847bd |
completed | April 6, 2026, 12:50 a.m. |
Created at: March 30, 2026, 9:10 p.m.