Triple
T12908059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Webster College |
E308777
|
entity |
| Predicate | hadAviationFacilities |
P13763
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Daniel Webster College, hadAviationFacilities, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadAviationFacilities Context triple: [Daniel Webster College, hadAviationFacilities, true]
-
A.
hasGeneralAviationFacilities
Indicates that a location or airport provides facilities and services specifically for general aviation operations.
-
B.
hasFormerMilitaryAirfield
Indicates that an entity possesses or is associated with an airfield that was previously used for military purposes but is no longer active as such.
-
C.
aircraftFacility
chosen
Indicates that a facility is designed, equipped, or used to support the operation, maintenance, or accommodation of aircraft.
-
D.
hasNightLandingFacilities
Indicates that a location or facility is equipped to support aircraft landings during nighttime conditions.
-
E.
airfieldBuilt
Indicates that an airfield has been constructed or established, typically specifying who built it, where, and/or when.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9719d4d1c8190a2c4f362e1772a73 |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.