Triple
T24118701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M4 railcar |
E597589
|
entity |
| Predicate | hasFixedFormation |
P142978
|
FINISHED |
| Object | multiple-car sets |
—
|
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: multiple-car sets | Statement: [M4 railcar, hasFixedFormation, multiple-car sets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFixedFormation Context triple: [M4 railcar, hasFixedFormation, multiple-car sets]
-
A.
hasCarFormation
chosen
Indicates that one entity is arranged or organized into a specific configuration or sequence of cars relative to another entity.
-
B.
containsFormation
Indicates that one entity spatially or structurally includes or encloses a formation as part of its extent or composition.
-
C.
appliesToFormationNumber
Indicates that something (such as a rule, condition, or attribute) is specifically associated with or relevant to a particular formation number.
-
D.
typicalFormation
Indicates the usual or characteristic way in which something is formed, structured, or comes into existence.
-
E.
numberOfFormations
Indicates the count of distinct formations associated with or involved in a given entity or context.
- 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_69e288c74200819098ab875b592cb39f |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1dee1a2608190870ade02495c5ebe |
completed | April 29, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11:05 p.m.