Triple
T20085723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babruvahana |
E500122
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Nagamani |
—
|
NE NERFINISHED |
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: Nagamani | Statement: [Babruvahana, associatedWith, Nagamani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagamani Context triple: [Babruvahana, associatedWith, Nagamani]
-
A.
Nagamani
chosen
Nagamani is a mythical, luminous gem in Indian folklore believed to be associated with serpents (nagas) and to possess powerful supernatural properties.
-
B.
Kamenari
Kamenari is a small coastal village in Montenegro known for its ferry crossing and scenic location on the Bay of Kotor.
-
C.
Mazani
Mazani is an alternative name for the Mazanderani language, an Iranian language spoken primarily along the southern coast of the Caspian Sea in northern Iran.
-
D.
Nagai
Nagai is a city in Yamagata Prefecture, Japan, known for its scenic riverside setting and surrounding mountainous countryside.
-
E.
Kanuma
Kanuma is a city in Japan’s Tochigi Prefecture known for its traditional wooden floats, historic streets, and the annual Kanuma Autumn Festival.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6655ae9ec8190bde2f17452639de8 |
completed | April 20, 2026, 5:41 p.m. |
Created at: April 11, 2026, 3:41 p.m.