Triple
T9580526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Main North Line |
E231156
|
entity |
| Predicate | lineLengthApproximate |
P25800
|
FINISHED |
| Object | 348 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: 348 km | Statement: [Main North Line, lineLengthApproximate, 348 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lineLengthApproximate Context triple: [Main North Line, lineLengthApproximate, 348 km]
-
A.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
B.
approximateLengthInMeters
chosen
Indicates the estimated or roughly measured length of something expressed in meters.
-
C.
hasLineLength
Indicates that one entity has, is characterized by, or is associated with a specific line length value.
-
D.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cbe79081909947b4d1389eb015 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.