Triple
T27394987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahanandi |
E691653
|
entity |
| Predicate | distanceFromKurnool_km |
P199740
|
FINISHED |
| Object | approximately 80 |
—
|
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 80 | Statement: [Mahanandi, distanceFromKurnool_km, approximately 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromKurnool_km Context triple: [Mahanandi, distanceFromKurnool_km, approximately 80]
-
A.
distanceFromNandyal_km
Indicates the distance, measured in kilometers, between a given place and Nandyal.
-
B.
distanceFromChandigarh_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Chandigarh.
-
C.
distanceFromKangra_km
Indicates the physical distance, measured in kilometers, between an entity and the location Kangra.
-
D.
distanceToKanyakumari
Indicates the spatial distance between a given location and Kanyakumari.
-
E.
distanceToChandigarh_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Chandigarh.
- 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_69ef5204f7048190bf226a129858fc5b |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69ff53389a0481908b2baeb43c6294f0 |
completed | May 9, 2026, 3:31 p.m. |
| PD | Predicate disambiguation | batch_69ff52e2b4b88190b38d160d771fe14b |
completed | May 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69ff5337e6f88190ae0418335477063c |
completed | May 9, 2026, 3:31 p.m. |
Created at: April 27, 2026, 12:27 p.m.