Triple
T170482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greenwich Mean Time |
E3109
|
entity |
| Predicate | usedInAviation |
P7940
|
FINISHED |
| Object | yes (historically, now largely UTC) |
—
|
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 (historically, now largely UTC) | Statement: [Greenwich Mean Time, usedInAviation, yes (historically, now largely UTC)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedInAviation Context triple: [Greenwich Mean Time, usedInAviation, yes (historically, now largely UTC)]
-
A.
aircraft
Indicates that an entity is an aircraft or functions in the role of an aircraft in the described context.
-
B.
aircraftManufacturerUsed
Indicates that a particular aircraft manufacturer was employed or utilized in relation to another entity, such as for production, design, or supply purposes.
-
C.
aircraftFlown
Indicates that an entity (typically a person or organization) operates or pilots a particular aircraft.
-
D.
primaryAircraftRole
Indicates the main operational function or mission type an aircraft is primarily designed or used to perform.
-
E.
aircraftType
Indicates the specific model or category of aircraft associated with an entity or event.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25a3f9d188190aacf5791273e2c65 |
completed | Feb. 28, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69a25667717c8190bac5108366e2178d |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a25a3ee6d08190a451ddba7504ce9b |
completed | Feb. 28, 2026, 3 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.