Triple
T15943671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Village of Bensenville |
E386629
|
entity |
| Predicate | hasFacilityTypeNearby |
P75188
|
FINISHED |
| Object | major international airport |
—
|
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: major international airport | Statement: [Village of Bensenville, hasFacilityTypeNearby, major international airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacilityTypeNearby Context triple: [Village of Bensenville, hasFacilityTypeNearby, major international airport]
-
A.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
B.
nearbyFacilityType
chosen
Indicates that a facility of a specified type is located close to a given reference entity or location.
-
C.
hasNearbySiteType
Indicates that one entity has another entity of a specified site type located in its close physical vicinity.
-
D.
hasNearbyInstitutionType
Indicates that an entity has at least one institution of a specified type located in its nearby geographic vicinity.
-
E.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d37cd88190ab50760f1783e20c |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.