Triple
T2210079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles de Gaulle |
E50893
|
entity |
| Predicate | flightDeckWidth |
P37490
|
FINISHED |
| Object | approximately 64.4 metres |
—
|
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: approximately 64.4 metres | Statement: [Charles de Gaulle, flightDeckWidth, approximately 64.4 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: flightDeckWidth Context triple: [Charles de Gaulle, flightDeckWidth, approximately 64.4 metres]
-
A.
flightDeckType
Indicates the specific configuration or design type of a vehicle’s flight deck (cockpit/control area).
-
B.
flightDeckFeature
Indicates that one entity is a feature, component, or element that is part of or present on the flight deck of another entity.
-
C.
aircraftLength
Indicates the physical longitudinal measurement of an aircraft from its nose to its tail.
-
D.
wingArea
Indicates the total surface area covered by an entity’s wing or wings.
-
E.
fuselageType
Indicates the specific structural or design category of an aircraft’s fuselage that an entity belongs to or uses.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc1b912c08190b9d7bc9230e49d1d |
completed | March 7, 2026, 6:12 a.m. |
Created at: March 4, 2026, 7:46 p.m.