Triple
T3471918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warrenton–Fauquier Airport |
E73279
|
entity |
| Predicate | hasTieDowns |
P49166
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Warrenton–Fauquier Airport, hasTieDowns, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTieDowns Context triple: [Warrenton–Fauquier Airport, hasTieDowns, yes]
-
A.
hasTrench
Indicates that one entity possesses, contains, or is characterized by a trench associated with it.
-
B.
maintainsTieWith
Indicates that one entity keeps an ongoing connection or relationship with another entity over time.
-
C.
hasClasps
Indicates that one entity is equipped with or features clasps that fasten, secure, or attach it to another entity or its parts.
-
D.
hasCoupling
Indicates that two entities are linked or joined together in a way that allows them to interact, transfer, or coordinate motion, energy, or information.
-
E.
typeOfRigging
Indicates the kind or configuration of rigging used in relation to an object or structure.
- 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_69ad85b2fed48190948c8765e453d270 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb3cc8488190b97c732e3f600a90 |
completed | March 8, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69adae07802c8190919c49b0e65b2797 |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adb21a437c81908bca88d5e123d744 |
completed | March 8, 2026, 5:30 p.m. |
Created at: March 8, 2026, 3:17 p.m.