Triple
T27394989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahanandi |
E691653
|
entity |
| Predicate | distanceFromHyderabad_km |
P31800
|
FINISHED |
| Object | approximately 220 |
—
|
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 220 | Statement: [Mahanandi, distanceFromHyderabad_km, approximately 220]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromHyderabad_km Context triple: [Mahanandi, distanceFromHyderabad_km, approximately 220]
-
A.
distanceToHyderabad
chosen
Indicates the spatial distance between a given entity’s location and the city of Hyderabad.
-
B.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
C.
distanceFromBangalore
Indicates the spatial distance separating a given entity or location from Bangalore.
-
D.
distanceFromNandyal_km
Indicates the distance, measured in kilometers, between a given place and Nandyal.
-
E.
distanceFromGurugram_km
Indicates the physical distance, measured in kilometers, between an entity’s location and Gurugram.
- 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_69ef5204f7048190bf226a129858fc5b |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69ff56ef0a5c8190ae729d66a8cf7fc4 |
completed | May 9, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ff539859c481909ec56310da418688 |
completed | May 9, 2026, 3:32 p.m. |
Created at: April 27, 2026, 12:27 p.m.