Triple
T16861004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karakoram Highway |
E409908
|
entity |
| Predicate | lengthInChina |
P125267
|
FINISHED |
| Object | about 494 kilometres |
—
|
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: about 494 kilometres | Statement: [Karakoram Highway, lengthInChina, about 494 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthInChina Context triple: [Karakoram Highway, lengthInChina, about 494 kilometres]
-
A.
rankingByLengthInChina
Indicates that entities are ordered or evaluated based on their length within the context of China.
-
B.
lengthInGermany
Indicates the extent or duration of something measured specifically within the geographic or jurisdictional boundaries of Germany.
-
C.
hasMainlandLengthApproxKm
Indicates the approximate length of an entity’s mainland portion, measured in kilometers.
-
D.
dimensionOfLength
Indicates that something represents or specifies a measurement along a single spatial extent (a length dimension).
-
E.
lengthInFrance
Indicates that the specified length or duration applies specifically within the context of France (e.g., under French conditions, jurisdiction, or territory).
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b502bc048190baa5a83015407080 |
completed | April 18, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e355722040819098830dabf207ecd6 |
completed | April 18, 2026, 9:57 a.m. |
Created at: April 10, 2026, 5:24 a.m.