Triple
T8832958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Sector (Geneva Airport) |
E210188
|
entity |
| Predicate | hasImmigrationStatus |
P36584
|
FINISHED |
| Object | Schengen internal flight handling |
—
|
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: Schengen internal flight handling | Statement: [French Sector (Geneva Airport), hasImmigrationStatus, Schengen internal flight handling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImmigrationStatus Context triple: [French Sector (Geneva Airport), hasImmigrationStatus, Schengen internal flight handling]
-
A.
immigrationStatusBeforeCitizenship
Indicates the legal immigration status an individual held prior to obtaining citizenship.
-
B.
legalStatusAtArrival
Indicates the legal status or classification an entity held at the time it first arrived at a particular place or jurisdiction.
-
C.
immigrationType
chosen
Indicates the specific category or classification of an individual’s immigration status or entry into a country.
-
D.
hasTypicalCitizenship
Indicates that an entity is generally or commonly a citizen of a specified country or jurisdiction.
-
E.
dualCitizenshipStatus
Indicates that an entity holds legal citizenship in two different countries simultaneously.
- 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_69ca8388549c819095fd94eadefbb007 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc605005788190a4df1fe317f3056a |
completed | April 1, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69cc5c23d08481908d8c9b0ad3d1dc00 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:47 p.m.