Triple
T7344567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumaoni |
E169342
|
entity |
| Predicate | hasDialects |
P4251
|
FINISHED |
| Object |
Rangloi
Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
|
E660058
|
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: Rangloi | Statement: [Kumaoni, hasDialects, Rangloi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rangloi Context triple: [Kumaoni, hasDialects, Rangloi]
-
A.
Rangat
Rangat is a coastal town and administrative hub located on Middle Andaman Island in the Andaman and Nicobar Islands, India.
-
B.
Lawai
Lawai is a small unincorporated community on the island of Kauai in Hawaii, known for its rural residential character and proximity to the island’s south shore attractions.
-
C.
Tillangchong
Tillangchong is a remote, sparsely inhabited island in India’s Nicobar archipelago, noted for its dense tropical forests and rich marine and bird life.
-
D.
Machang
Machang is a town and administrative district in the Malaysian state of Kelantan, known for its semi-urban character and role as a local commercial and educational hub.
-
E.
Sairang
Sairang is a small town in the Indian state of Mizoram, known for its scenic riverside setting and role as a transport and trading hub near the state capital Aizawl.
- 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: Rangloi Triple: [Kumaoni, hasDialects, Rangloi]
Generated description
Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rangloi Target entity description: Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
-
A.
Rangat
Rangat is a coastal town and administrative hub located on Middle Andaman Island in the Andaman and Nicobar Islands, India.
-
B.
Lawai
Lawai is a small unincorporated community on the island of Kauai in Hawaii, known for its rural residential character and proximity to the island’s south shore attractions.
-
C.
Tillangchong
Tillangchong is a remote, sparsely inhabited island in India’s Nicobar archipelago, noted for its dense tropical forests and rich marine and bird life.
-
D.
Machang
Machang is a town and administrative district in the Malaysian state of Kelantan, known for its semi-urban character and role as a local commercial and educational hub.
-
E.
Sairang
Sairang is a small town in the Indian state of Mizoram, known for its scenic riverside setting and role as a transport and trading hub near the state capital Aizawl.
- 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_69c68a57710481909f0c1f3c6ebdb6f2 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0eeb30081909d25704ac9b49d0e |
completed | March 27, 2026, 9:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c802b24194819096b796de15d66ed2 |
completed | March 28, 2026, 4:32 p.m. |
| NEDg | Description generation | batch_69c8044ce6e88190aa36461ac729235b |
completed | March 28, 2026, 4:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8050e70108190b2cc0d2d30ddf139 |
completed | March 28, 2026, 4:42 p.m. |
Created at: March 27, 2026, 3:05 p.m.