Triple
T3069476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fieseler Fi 103 |
E62185
|
entity |
| Predicate | engineModel |
P2092
|
FINISHED |
| Object |
Argus As 014
Argus As 014 was a German World War II pulsejet engine best known for powering the V-1 flying bomb.
|
E324070
|
NE FINISHED |
How this triple was built (4 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 014 | Statement: [Fieseler Fi 103, engineModel, Argus As 014]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Argus As 014 Context triple: [Fieseler Fi 103, engineModel, Argus As 014]
-
A.
Argus
Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
-
B.
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.
-
C.
Argos
Argos is the common nickname for the Toronto Argonauts, a professional Canadian Football League team based in Toronto.
-
D.
Argos
Argos is a major UK-based catalogue and online retailer known for offering a wide range of household goods, electronics, toys, and more through both physical stores and digital channels.
-
E.
Argos
Argos is one of the oldest continuously inhabited cities in Greece, located in the Peloponnese and historically significant as a major center of ancient Greek civilization.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Argus As 014 Triple: [Fieseler Fi 103, engineModel, Argus As 014]
Generated description
Argus As 014 was a German World War II pulsejet engine best known for powering the V-1 flying bomb.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Argus As 014 Target entity description: Argus As 014 was a German World War II pulsejet engine best known for powering the V-1 flying bomb.
-
A.
Argus
Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
-
B.
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.
-
C.
Argos
Argos is the common nickname for the Toronto Argonauts, a professional Canadian Football League team based in Toronto.
-
D.
Argos
Argos is a major UK-based catalogue and online retailer known for offering a wide range of household goods, electronics, toys, and more through both physical stores and digital channels.
-
E.
Argos
Argos is one of the oldest continuously inhabited cities in Greece, located in the Peloponnese and historically significant as a major center of ancient Greek civilization.
- F. None of above. chosen
Provenance (5 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada100f0b8819095da366fdc6803a8 |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f87f6a3881908ae313f62ff13159 |
completed | March 11, 2026, 11:19 p.m. |
| NEDg | Description generation | batch_69b1f908ee3081909126bc3797658fca |
completed | March 11, 2026, 11:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f99f7aec8190bb35ef4ddf64735c |
completed | March 11, 2026, 11:24 p.m. |
Created at: March 8, 2026, 3:02 p.m.