Triple
T3553669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salzgitter |
E75169
|
entity |
| Predicate | hasCompany |
P1287
|
FINISHED |
| Object | Salzgitter AG |
E75169
|
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: Salzgitter AG | Statement: [Salzgitter, hasCompany, Salzgitter AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salzgitter AG Context triple: [Salzgitter, hasCompany, Salzgitter AG]
-
A.
Salzgitter
chosen
Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
-
B.
Preussag AG
Preussag AG was a former German industrial and mining conglomerate that transformed in the 1990s into a tourism-focused company, eventually becoming today’s TUI Group.
-
C.
S7 Group
S7 Group is a Russian aviation holding company best known for owning and operating S7 Airlines and related air transport businesses.
-
D.
Borsigwerke
Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
-
E.
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.
- 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_69ad85d33c6c819081d5ac1df13b5680 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc05394888190b59fafda97b49beb |
completed | March 8, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38beef8b4819090109ab89e9671d6 |
completed | March 13, 2026, 4 a.m. |
Created at: March 8, 2026, 3:20 p.m.