Triple
T3508467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red River (Vietnam) |
E74138
|
entity |
| Predicate | lengthInVietnam |
P33049
|
FINISHED |
| Object | approximately 510 km |
—
|
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 510 km | Statement: [Red River (Vietnam), lengthInVietnam, approximately 510 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthInVietnam Context triple: [Red River (Vietnam), lengthInVietnam, approximately 510 km]
-
A.
distanceFromHanoi
Indicates the spatial distance between a given location and Hanoi.
-
B.
lengthInKm
chosen
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
-
C.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
D.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
E.
lengthVariesBy
Indicates that the length of one entity changes or differs depending on another specified factor or condition.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0cc394819087a9b598023f4f93 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.