Triple
T33999245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aerobús express airport bus |
E871768
|
entity |
| Predicate | typicalUserUseCase |
P2529
|
FINISHED |
| Object | transfer from airport to Barcelona city centre |
—
|
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: transfer from airport to Barcelona city centre | Statement: [Aerobús express airport bus, typicalUserUseCase, transfer from airport to Barcelona city centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUserUseCase Context triple: [Aerobús express airport bus, typicalUserUseCase, transfer from airport to Barcelona city centre]
-
A.
frequentUseCase
Indicates a situation, scenario, or pattern of use that occurs regularly or more often than others in relation to the subject.
-
B.
usageType
chosen
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
C.
typicalInteraction
Indicates the usual or most common way in which two entities interact or relate to each other.
-
D.
typicalUsagePhase
Indicates the phase or stage in which something is most commonly or characteristically used.
-
E.
typicalUsageFormat
Indicates the usual or standard way in which something is expressed, presented, or formatted in practice.
- 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_69f3499f8cbc81908de6ec89fa91ea8f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:50 a.m.