Triple
T21053636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruvu languages |
E518651
|
entity |
| Predicate | hasMemberLanguage |
P7390
|
FINISHED |
| Object |
Sagara
Sagara is a Bantu language of the Ruvu group spoken primarily in parts of Tanzania.
|
E1468474
|
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: Sagara | Statement: [Ruvu languages, hasMemberLanguage, Sagara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sagara Context triple: [Ruvu languages, hasMemberLanguage, Sagara]
-
A.
Sagara
Sagara is a town in the Shivamogga district of Karnataka, India, known for its proximity to Jog Falls and the Western Ghats.
-
B.
Sagara
Sagara is a legendary king in Hindu mythology, renowned as an ancestor of Lord Rama and for his role in the origins of the sacred Ganges River’s descent to earth.
-
C.
Ariake
Ariake is a waterfront district in Tokyo known for its large exhibition centers, sports venues, and modern urban development on the artificial islands of Tokyo Bay.
-
D.
Akashi
Akashi was a Japanese warship that served in the Imperial Japanese Navy around the time of the Russo-Japanese War.
-
E.
Akashi
Akashi is a coastal city in western Japan known for its historic castle, views of the Akashi Kaikyō Strait, and its specialty dish akashiyaki.
- 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: Sagara Triple: [Ruvu languages, hasMemberLanguage, Sagara]
Generated description
Sagara is a Bantu language of the Ruvu group spoken primarily in parts of Tanzania.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sagara Target entity description: Sagara is a Bantu language of the Ruvu group spoken primarily in parts of Tanzania.
-
A.
Sagara
Sagara is a town in the Shivamogga district of Karnataka, India, known for its proximity to Jog Falls and the Western Ghats.
-
B.
Sagara
Sagara is a legendary king in Hindu mythology, renowned as an ancestor of Lord Rama and for his role in the origins of the sacred Ganges River’s descent to earth.
-
C.
Ariake
Ariake is a waterfront district in Tokyo known for its large exhibition centers, sports venues, and modern urban development on the artificial islands of Tokyo Bay.
-
D.
Akashi
Akashi was a Japanese warship that served in the Imperial Japanese Navy around the time of the Russo-Japanese War.
-
E.
Akashi
Akashi is a coastal city in western Japan known for its historic castle, views of the Akashi Kaikyō Strait, and its specialty dish akashiyaki.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7e087c81908712ddc63e8b1e6c |
completed | April 21, 2026, 4:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a096dae71a48190b3b356783babb119 |
completed | May 17, 2026, 7:26 a.m. |
| NEDg | Description generation | batch_6a096e0ddf1c819096d3ee89517d07eb |
completed | May 17, 2026, 7:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a096e70ba188190b9ee4fbc588d790c |
completed | May 17, 2026, 7:29 a.m. |
Created at: April 16, 2026, 2:36 p.m.