Triple
T13031500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Combino tram |
E326450
|
entity |
| Predicate | typicalSectionCount |
P82381
|
FINISHED |
| Object | 3 to 7 sections |
—
|
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: 3 to 7 sections | Statement: [Combino tram, typicalSectionCount, 3 to 7 sections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSectionCount Context triple: [Combino tram, typicalSectionCount, 3 to 7 sections]
-
A.
typicalSection
Indicates that one section is a standard, representative, or commonly occurring instance within a broader set or structure of sections.
-
B.
sectionCountApproximate
chosen
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
C.
slotCountTypical
Indicates the usual or standard number of slots associated with an entity under normal conditions.
-
D.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
E.
typicalNumberOfSelections
Indicates the usual or expected count of selections made in a given choice or selection process.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efe72348190b52fb4068f5fb829 |
completed | April 10, 2026, 10:51 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:54 p.m.