Triple
T14492969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russell Offices |
E359411
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
R2 building
R2 building is a key structure within the Russell Offices complex in Canberra, serving as part of Australia’s central defence administrative facilities.
|
E1102109
|
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: R2 building | Statement: [Russell Offices, hasPart, R2 building]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: R2 building Context triple: [Russell Offices, hasPart, R2 building]
-
A.
R7 building
The R7 building is a government office block in Oslo that forms part of Norway’s central Government Quarter complex.
-
B.
R5 building
The R5 building is a key Norwegian government office complex in Oslo that houses several central ministries and administrative functions.
-
C.
R2 Sud
R2 Sud is a suburban commuter rail line in Catalonia, Spain, that connects Barcelona with southern coastal towns and cities.
-
D.
R4 building
The R4 building is a government office block in Oslo that forms part of Norway's central Government Quarter complex.
-
E.
R2 Nord
R2 Nord is a commuter rail service line that operates in the northern sector of its regional rail network, connecting suburban areas with major urban centers.
- 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: R2 building Triple: [Russell Offices, hasPart, R2 building]
Generated description
R2 building is a key structure within the Russell Offices complex in Canberra, serving as part of Australia’s central defence administrative facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: R2 building Target entity description: R2 building is a key structure within the Russell Offices complex in Canberra, serving as part of Australia’s central defence administrative facilities.
-
A.
R7 building
The R7 building is a government office block in Oslo that forms part of Norway’s central Government Quarter complex.
-
B.
R5 building
The R5 building is a key Norwegian government office complex in Oslo that houses several central ministries and administrative functions.
-
C.
R2 Sud
R2 Sud is a suburban commuter rail line in Catalonia, Spain, that connects Barcelona with southern coastal towns and cities.
-
D.
R4 building
The R4 building is a government office block in Oslo that forms part of Norway's central Government Quarter complex.
-
E.
R2 Nord
R2 Nord is a commuter rail service line that operates in the northern sector of its regional rail network, connecting suburban areas with major urban centers.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de930f34d08190b4b30e54e2f702ed |
completed | April 14, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d9544cc81908105554f212a9b8c |
completed | May 8, 2026, 4:59 a.m. |
| NEDg | Description generation | batch_69fd6e75155081908cec8cac1101c17e |
completed | May 8, 2026, 5:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd6f38ea788190a957419ca1971b15 |
completed | May 8, 2026, 5:06 a.m. |
Created at: April 10, 2026, 1:20 a.m.