Triple
T8741833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S7 Group |
E207520
|
entity |
| Predicate | ownsSubsidiary |
P9212
|
FINISHED |
| Object |
S7 Technics
S7 Technics is a Russian aircraft maintenance, repair, and overhaul (MRO) company that services both S7 Airlines and other carriers.
|
E754446
|
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: S7 Technics | Statement: [S7 Group, ownsSubsidiary, S7 Technics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: S7 Technics Context triple: [S7 Group, ownsSubsidiary, S7 Technics]
-
A.
S7
S7 is a Berlin S-Bahn rapid transit line that runs across the city, connecting key districts between Potsdam and Ahrensfelde.
-
B.
S7
S7 is the IATA airline designator for S7 Airlines, a major Russian carrier based in Novosibirsk.
-
C.
Siemens S70
The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
-
D.
Siemens SD100
The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
-
E.
Siemens SD-160
The Siemens SD-160 is a high-floor light rail vehicle widely used in North American transit systems, including Calgary’s CTrain, known for its modular design and reliable urban service.
- 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: S7 Technics Triple: [S7 Group, ownsSubsidiary, S7 Technics]
Generated description
S7 Technics is a Russian aircraft maintenance, repair, and overhaul (MRO) company that services both S7 Airlines and other carriers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: S7 Technics Target entity description: S7 Technics is a Russian aircraft maintenance, repair, and overhaul (MRO) company that services both S7 Airlines and other carriers.
-
A.
S7
S7 is the IATA airline designator for S7 Airlines, a major Russian carrier based in Novosibirsk.
-
B.
S7
S7 is a Berlin S-Bahn rapid transit line that runs across the city, connecting key districts between Potsdam and Ahrensfelde.
-
C.
Siemens S70
The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
-
D.
Siemens SD100
The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
-
E.
Siemens SD-160
The Siemens SD-160 is a high-floor light rail vehicle widely used in North American transit systems, including Calgary’s CTrain, known for its modular design and reliable urban service.
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d6fd5dc8190906b7147f27c5d46 |
completed | March 31, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf42f282e48190ad158063e265e0f0 |
completed | April 3, 2026, 4:32 a.m. |
| NEDg | Description generation | batch_69cf4433605c8190991f95d19726cab8 |
completed | April 3, 2026, 4:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf44c20b408190b622d18f78277802 |
completed | April 3, 2026, 4:40 a.m. |
Created at: March 30, 2026, 6:38 p.m.