Triple
T25332693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lunglei |
E635192
|
entity |
| Predicate | distanceFromAizawl_km |
P179990
|
FINISHED |
| Object | approximately 165 |
—
|
LITERAL 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: approximately 165 | Statement: [Lunglei, distanceFromAizawl_km, approximately 165]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromAizawl_km Context triple: [Lunglei, distanceFromAizawl_km, approximately 165]
-
A.
distanceFromGuwahati_km
Indicates the physical distance, measured in kilometers, between an entity and the location of Guwahati.
-
B.
distanceFromMawlamyine_km
Indicates the distance, measured in kilometers, between a given place or entity and Mawlamyine.
-
C.
distanceFromShillong
Indicates the spatial distance between a given entity or location and Shillong.
-
D.
distanceToKohima_km
Indicates the physical distance, measured in kilometers, from a given entity or location to Kohima.
-
E.
distanceToSiliguri_km
Indicates the physical distance, measured in kilometers, between a given location and Siliguri.
- F. None of above. chosen
Provenance (4 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_69e75a9908108190a95427a97020632a |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
| PDg | Predicate description generation | batch_69f730890a008190a882f7828f1c9162 |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 21, 2026, 1:30 p.m.