Triple
T1916092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagaland |
E40020
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Kohima |
E162720
|
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: Kohima | Statement: [Nagaland, capital, Kohima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kohima Context triple: [Nagaland, capital, Kohima]
-
A.
Kohima
chosen
Kohima is the hilly capital city of Nagaland, known for its strategic role in World War II and its rich Naga cultural heritage.
-
B.
Mawlynnong
Mawlynnong is a village in the Indian state of Meghalaya renowned for its exceptional cleanliness, eco-friendly practices, and living root bridges.
-
C.
Imphal
Imphal is the capital city of the Indian state of Manipur, known for its scenic valleys, historic Kangla Fort, and cultural significance in Northeast India.
-
D.
Aizawl
Aizawl is the hilly capital city of the Indian state of Mizoram, known for its scenic landscapes and vibrant Mizo culture in Northeast India.
-
E.
Kalimpong
Kalimpong is a hill station and town in the eastern Himalayas of India, known for its scenic views, Buddhist monasteries, and colonial-era architecture.
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1e517e8819086e4bf5a305aeb25 |
completed | March 7, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3da81308190a49844a8ac2997da |
completed | March 8, 2026, 10:10 p.m. |
Created at: March 4, 2026, 7:35 p.m.