Triple
T14431132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fieseler Fi 156 Storch |
E357829
|
entity |
| Predicate | engineType |
P1585
|
FINISHED |
| Object | Argus As 10 |
E499435
|
NE 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: Argus As 10 | Statement: [Fieseler Fi 156 Storch, engineType, Argus As 10]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Argus As 10 Context triple: [Fieseler Fi 156 Storch, engineType, Argus As 10]
-
A.
Argus As 10
chosen
The Argus As 10 is a German air-cooled inverted V8 aircraft engine widely used in light aircraft and trainers during the 1930s and World War II.
-
B.
Argus As 014
Argus As 014 was a German World War II pulsejet engine best known for powering the V-1 flying bomb.
-
C.
Argus
Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
-
D.
Argus
Argus is an early distributed programming language known for pioneering concepts in fault-tolerant, distributed systems and influencing modern object-oriented and concurrent programming.
-
E.
Argos
Argos is the common nickname for the Toronto Argonauts, a professional Canadian Football League team based in Toronto.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914570f08190b1c7c1c57a0cb476 |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bd3e6c48190b4fc3794202a0c3f |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.