Triple
T2320499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sd.Kfz. 251 |
E51167
|
entity |
| Predicate | designedBy |
P184
|
FINISHED |
| Object | Hanomag |
E256148
|
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: Hanomag | Statement: [Sd.Kfz. 251, designedBy, Hanomag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanomag Context triple: [Sd.Kfz. 251, designedBy, Hanomag]
-
A.
Hanomag
chosen
Hanomag was a German engineering and vehicle manufacturing company best known for producing military half-tracks and civilian tractors in the first half of the 20th century.
-
B.
Yuasa
Yuasa is a historic coastal town in Japan renowned as the birthplace of traditional soy sauce production.
-
C.
Nisshoki
Nisshoki, more commonly known as the Hinomaru, is the national flag of Japan featuring a red sun disc centered on a white field.
-
D.
Hama
Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
-
E.
Yoshimura
Yoshimura is a Japanese surname borne by various notable individuals across fields such as politics, sports, and the arts.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc632474c8190972b4611a3a4ff8f |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae96139e688190847bfa872bd08ed4 |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:49 p.m.