Triple
T4087018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Highland bagpipe |
E87611
|
entity |
| Predicate | hasDroneConfiguration |
P13644
|
FINISHED |
| Object | one bass drone and two tenor drones |
—
|
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: one bass drone and two tenor drones | Statement: [Great Highland bagpipe, hasDroneConfiguration, one bass drone and two tenor drones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDroneConfiguration Context triple: [Great Highland bagpipe, hasDroneConfiguration, one bass drone and two tenor drones]
-
A.
hasOnboardRobot
Indicates that one entity (typically a vehicle, device, or platform) is equipped with or carries a robot on board.
-
B.
hasWingConfiguration
Indicates how an entity’s wings are arranged, structured, or configured relative to its body or to each other.
-
C.
hasConfiguration
chosen
Indicates that an entity is associated with or defined by a particular configuration or setup.
-
D.
hasRunwayConfiguration
Indicates a specific arrangement or setup of runways associated with an airport, airfield, or similar facility.
-
E.
hasPylon
Indicates that one entity possesses, includes, or is equipped with a pylon as part of its structure or configuration.
- 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_69aed94425148190be337845d56fac22 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc7ceeb48190807f0f5078ccfa12 |
completed | March 9, 2026, 4:59 p.m. |
| PD | Predicate disambiguation | batch_69aef909c9c88190b09d48dad325a83c |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:39 p.m.