Triple
T721690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ledo Road |
E14629
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Assam |
E4843
|
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: Assam | Statement: [Ledo Road, region, Assam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assam Context triple: [Ledo Road, region, Assam]
-
A.
Assam
chosen
Assam is a northeastern region of the Indian subcontinent known for its tea plantations, rich biodiversity, and distinct cultural heritage.
-
B.
Tripura
Tripura is a small, hilly state in northeastern India known for its diverse tribal cultures, historical palaces, and dense forests.
-
C.
Arunachal Pradesh
Arunachal Pradesh is a northeastern Indian state known for its mountainous terrain, diverse indigenous cultures, and strategic location along the borders with China, Bhutan, and Myanmar.
-
D.
West Bengal
West Bengal is an eastern Indian state known for its cultural heritage, literature, and the metropolis of Kolkata (formerly Calcutta).
-
E.
Meghalaya
Meghalaya is a hilly state in northeastern India known for its heavy rainfall, lush forests, and diverse indigenous cultures.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a591124c8190842e7ef18b064198 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d71a25c81908de9b9e59affb79f |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:37 p.m.