Triple
T29395655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nohkalikai Falls |
E745492
|
entity |
| Predicate | rankingByHeightInIndia |
P177332
|
FINISHED |
| Object | among tallest waterfalls in India |
—
|
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: among tallest waterfalls in India | Statement: [Nohkalikai Falls, rankingByHeightInIndia, among tallest waterfalls in India]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByHeightInIndia Context triple: [Nohkalikai Falls, rankingByHeightInIndia, among tallest waterfalls in India]
-
A.
rankByLengthInIndia
Indicates an ordering of items based on their length specifically within the context or boundaries of India.
-
B.
rankByHeightInMaharashtra
Indicates the relative ordering of entities based on their height within the geographic region of Maharashtra.
-
C.
rankWithinBritishIndia
Indicates the relative hierarchical position or status an entity held within the administrative, military, or social ranking system of British India.
-
D.
rankByPopulationInIndia
Indicates the relative ordering of entities based on their population size within India.
-
E.
rankingByHeightInJapan
Indicates the relative order of entities based on their height specifically within the context of Japan.
- 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_69f0a79dfabc81908755382ee47791e2 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f6fb93224881908fc66fe76115fcdb |
completed | May 3, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f6fb17d5ec81909091e37e1ddbe577 |
completed | May 3, 2026, 7:36 a.m. |
Created at: April 28, 2026, 2:46 p.m.