Triple
T14972935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamba River |
E373368
|
entity |
| Predicate | rankInKeralaByLength |
P18229
|
FINISHED |
| Object | third longest river in Kerala |
—
|
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: third longest river in Kerala | Statement: [Pamba River, rankInKeralaByLength, third longest river in Kerala]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInKeralaByLength Context triple: [Pamba River, rankInKeralaByLength, third longest river in Kerala]
-
A.
rankByLengthInIndia
Indicates an ordering of items based on their length specifically within the context or boundaries of India.
-
B.
rankingByLengthInPeninsularMalaysia
Indicates a ranking relationship among items based on their lengths, specifically within the context of Peninsular Malaysia.
-
C.
rankByLengthInWorld
Indicates ordering entities within a given world or context based on their length, from shortest to longest or vice versa.
-
D.
rankByLength
chosen
Indicates ordering a set of items based on their length, typically from shortest to longest or vice versa.
-
E.
rankByPopulationInIndia
Indicates the relative ordering of entities based on their population size within India.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e767608190940eb6f16ea97451 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:50 a.m.