Triple
T775342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yellow River |
E16374
|
entity |
| Predicate | rankingByLengthInChina |
P19643
|
FINISHED |
| Object | second-longest river in China |
—
|
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: second-longest river in China | Statement: [Yellow River, rankingByLengthInChina, second-longest river in China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByLengthInChina Context triple: [Yellow River, rankingByLengthInChina, second-longest river in China]
-
A.
rankByLengthInIndia
Indicates an ordering of items based on their length specifically within the context or boundaries of India.
-
B.
rankByLength
Indicates ordering a set of items based on their length, typically from shortest to longest or vice versa.
-
C.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
D.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
E.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a74da7648190adfad56717d564df |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a50a443481909ae3662764ee69a4 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a74c81bc81909f4ac9c1677b09c2 |
completed | March 1, 2026, 8:53 p.m. |
Created at: March 1, 2026, 7:37 p.m.