Triple
T29789074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SNCF Class Z 26500 |
E756346
|
entity |
| Predicate | lengthPerSet_m |
P140284
|
FINISHED |
| Object | approximately 100 |
—
|
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 100 | Statement: [SNCF Class Z 26500, lengthPerSet_m, approximately 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthPerSet_m Context triple: [SNCF Class Z 26500, lengthPerSet_m, approximately 100]
-
A.
totalLength_m
chosen
Indicates the overall measured length of something expressed in meters.
-
B.
lengthRegime
Indicates a specific range or category of length within which something operates, is measured, or is classified.
-
C.
inningLength
Indicates the duration or number of units (e.g., outs, minutes, or pitches) that make up a single inning in a game or sporting event.
-
D.
properLengthMeasuredIn
Indicates that the proper (intrinsic or rest-frame) length of an entity is expressed using a specified unit of measurement.
-
E.
lengthInMinutes
Indicates the duration of something expressed as a number of minutes.
- 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_69f22451fb748190bbdbab401280affb |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f674e137608190af63dfd02ce106ac |
completed | May 2, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69f66ac1a4fc81909740d2e52fbe6970 |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 29, 2026, 5:11 p.m.