Triple
T9645128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TAM medium tank |
E233174
|
entity |
| Predicate | designer |
P184
|
FINISHED |
| Object | Thyssen-Henschel |
E40709
|
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: Thyssen-Henschel | Statement: [TAM medium tank, designer, Thyssen-Henschel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thyssen-Henschel Context triple: [TAM medium tank, designer, Thyssen-Henschel]
-
A.
Borsigwerke
Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
-
B.
Krupp (company)
Krupp (company) was a major German industrial conglomerate best known for its steel production and armaments manufacturing, playing a central role in both World Wars and in the development of heavy industry in Germany.
-
C.
Gothaer Waggonfabrik
Gothaer Waggonfabrik was a German industrial company best known for producing military aircraft, including heavy bombers, during World War I.
-
D.
Krauss-Maffei Wegmann
chosen
Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
-
E.
Rheinmetall
Rheinmetall is a major German defense and automotive company best known for producing advanced military technologies, including the main armament and systems for modern battle tanks.
- 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_69ca848b31648190b57aa55da20285be |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9b7fd2308190803a196ecdc80d76 |
completed | April 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18258489081909f0b328e223777fc |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:12 p.m.