Triple
T15738873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Varuna |
E381549
|
entity |
| Predicate | mount |
P18930
|
FINISHED |
| Object | makara |
E448849
|
NE FINISHED |
How this triple was built (2 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: makara | Statement: [Varuna, mount, makara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: makara Context triple: [Varuna, mount, makara]
-
A.
makara
chosen
Makara is a mythical sea creature in Hindu and Buddhist traditions, often depicted as a composite aquatic beast and used as a vehicle or emblem of water deities.
-
B.
MaK
MaK is a German engineering and manufacturing company historically known for producing heavy machinery, locomotives, and military vehicles.
-
C.
Makadara
Makadara is a residential and commercial neighborhood in Nairobi, Kenya, known for its dense population, vibrant local markets, and mix of low- to middle-income housing.
-
D.
Mararaba
Mararaba is a rapidly growing suburban town near Abuja in central Nigeria, known for its dense population and heavy commuter traffic.
-
E.
Sukari
"Sukari" is a popular Tanzanian Bongo Flava song by singer Zuchu that gained widespread acclaim across East Africa.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fd816308190a297986ee7e5554c |
completed | April 16, 2026, 2:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff830336248190a8bbd8153dd95daa |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:46 a.m.