Triple
T686898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yangtze River |
E13303
|
entity |
| Predicate | rankByLength |
P18229
|
FINISHED |
| Object | longest river in Asia |
—
|
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: longest river in Asia | Statement: [Yangtze River, rankByLength, longest river in Asia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankByLength Context triple: [Yangtze River, rankByLength, longest river in Asia]
-
A.
rankBySize
Indicates that entities are ordered or compared based on their size.
-
B.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
C.
rankByLengthInIndia
Indicates an ordering of items based on their length specifically within the context or boundaries of India.
-
D.
rankByLengthInEurope
Indicates that entities are ordered or compared based on their length specifically within the context of Europe.
-
E.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d2048d48190ab99ab59accb6909 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a4a0f405748190ba72a9cfe946a8ec |
completed | March 1, 2026, 8:26 p.m. |
Created at: March 1, 2026, 7:36 p.m.