Triple
T10192638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Junkers Ju 90 |
E238075
|
entity |
| Predicate | militaryMarkings |
P29893
|
FINISHED |
| Object | Luftwaffe markings |
—
|
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: Luftwaffe markings | Statement: [Junkers Ju 90, militaryMarkings, Luftwaffe markings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryMarkings Context triple: [Junkers Ju 90, militaryMarkings, Luftwaffe markings]
-
A.
aircraftMarking
chosen
Indicates a relationship where a marking, symbol, or identifier is applied to or displayed on an aircraft.
-
B.
hasMilitaryDesignation
Indicates that an entity is assigned a specific military-related code, title, or classification.
-
C.
mayHaveMarkings
Indicates that an entity is permitted or able to possess certain markings or distinguishing signs.
-
D.
distinctiveMarking
Indicates that one entity bears a unique or distinguishing visual feature or pattern that sets it apart from others.
-
E.
militaryCharacteristic
Indicates that one entity possesses a specific military-related attribute, quality, or feature in relation to another entity or context.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc4fb808190aae2e4b84be96f83 |
completed | April 2, 2026, 4:17 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:13 p.m.