Triple
T20563245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 2 (Munich Airport) |
E504896
|
entity |
| Predicate | hasGateArea |
P4365
|
FINISHED |
| Object | multiple boarding gates |
—
|
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 boarding gates | Statement: [Terminal 2 (Munich Airport), hasGateArea, multiple boarding gates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGateArea Context triple: [Terminal 2 (Munich Airport), hasGateArea, multiple boarding gates]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasGateNumber
Indicates that an entity (such as a flight or departure) is associated with a specific gate number.
-
C.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
D.
hasGate
chosen
Indicates that one entity possesses, includes, or is equipped with a gate as part of its structure or configuration.
-
E.
hasGlassArea
Indicates that an object or structure possesses a surface or region made of glass.
- 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a7a0a0488190a534050b40ff47da |
completed | April 20, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69e59ff0116c8190a163ff28ed01430a |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:39 a.m.