Triple
T9748524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umiam Lake |
E236376
|
entity |
| Predicate | approxDistanceInKilometresFromShillong |
P83907
|
FINISHED |
| Object | about 15 |
—
|
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: about 15 | Statement: [Umiam Lake, approxDistanceInKilometresFromShillong, about 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxDistanceInKilometresFromShillong Context triple: [Umiam Lake, approxDistanceInKilometresFromShillong, about 15]
-
A.
distanceFromShillong
chosen
Indicates the spatial distance between a given entity or location and Shillong.
-
B.
distanceToSiliguri_km
Indicates the physical distance, measured in kilometers, between a given location and Siliguri.
-
C.
distanceFromGuwahati_km
Indicates the physical distance, measured in kilometers, between an entity and the location of Guwahati.
-
D.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
-
E.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
- F. None of above.
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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f68f8b88190b44babf5ae17dfef |
completed | April 1, 2026, 10:42 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:23 p.m.