Triple
T6210380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 1 (Leonardo da Vinci–Fiumicino Airport) |
E138851
|
entity |
| Predicate | handlesOperationType |
P68920
|
FINISHED |
| Object | check-in operations |
—
|
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: check-in operations | Statement: [Terminal 1 (Leonardo da Vinci–Fiumicino Airport), handlesOperationType, check-in operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: handlesOperationType Context triple: [Terminal 1 (Leonardo da Vinci–Fiumicino Airport), handlesOperationType, check-in operations]
-
A.
hasOperationType
Indicates the specific kind or category of operation associated with an entity or process.
-
B.
typeOfOperation
Indicates the specific kind or category of operation being performed or referenced in a given context.
-
C.
performsOperation
Indicates that one entity carries out or executes a specific operation on or for another entity.
-
D.
operationType
Indicates the specific kind of operation or action being performed or recorded in the relationship between entities.
-
E.
operatedForClientType
Indicates that an operation or service is performed specifically on behalf of, or tailored to, a particular type or category of client.
- F. None of above. chosen
Provenance (4 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062896f3881909f264bb45badc5d0 |
completed | March 22, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69c055fdea3c81908f5d910f0d36234a |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:21 p.m.