Triple
T11999418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingdom of Ruhuna |
E285617
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Magama
Magama was an ancient city in southern Sri Lanka that served as the political and administrative center of the Kingdom of Ruhuna.
|
E958966
|
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: Magama | Statement: [Kingdom of Ruhuna, capital, Magama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magama Context triple: [Kingdom of Ruhuna, capital, Magama]
-
A.
Gamasa
Gamasa is a coastal city in Egypt’s Dakahlia Governorate, known for its Mediterranean shoreline and role as a regional urban center.
-
B.
Maggu
Maggu is a character from the Indian comic series "Chacha Chaudhary," known as one of the recurring goons who often clash with the protagonists.
-
C.
Baldeo
Baldeo is a town in India’s Braj region, known for its religious significance and association with Krishna-related traditions.
-
D.
Masmo
Masmo is a residential district in the southern suburbs of Stockholm, Sweden, known for its metro station on the red line and proximity to green areas and Lake Mälaren.
-
E.
Hamaki
Hamaki was the Japanese nickname, meaning "cigar," for the Mitsubishi G4M, a long-range World War II bomber known for its distinctive cylindrical fuselage and lack of armor.
- 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: Magama Triple: [Kingdom of Ruhuna, capital, Magama]
Generated description
Magama was an ancient city in southern Sri Lanka that served as the political and administrative center of the Kingdom of Ruhuna.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Magama Target entity description: Magama was an ancient city in southern Sri Lanka that served as the political and administrative center of the Kingdom of Ruhuna.
-
A.
Gamasa
Gamasa is a coastal city in Egypt’s Dakahlia Governorate, known for its Mediterranean shoreline and role as a regional urban center.
-
B.
Maggu
Maggu is a character from the Indian comic series "Chacha Chaudhary," known as one of the recurring goons who often clash with the protagonists.
-
C.
Baldeo
Baldeo is a town in India’s Braj region, known for its religious significance and association with Krishna-related traditions.
-
D.
Masmo
Masmo is a residential district in the southern suburbs of Stockholm, Sweden, known for its metro station on the red line and proximity to green areas and Lake Mälaren.
-
E.
Hamaki
Hamaki was the Japanese nickname, meaning "cigar," for the Mitsubishi G4M, a long-range World War II bomber known for its distinctive cylindrical fuselage and lack of armor.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c26d7881909b67a31d04882eb5 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f472917ed08190a872d9e5663d5ed5 |
completed | May 1, 2026, 9:29 a.m. |
| NEDg | Description generation | batch_69f47b7e4a40819085680c48eed5418a |
completed | May 1, 2026, 10:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f47df40a8c8190bd7350ba27f57214 |
completed | May 1, 2026, 10:18 a.m. |
Created at: April 8, 2026, 9:46 p.m.