Triple
T1061423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tycho Brahe |
E22915
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Scania
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
|
E138516
|
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: Scania | Statement: [Tycho Brahe, placeOfBirth, Scania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scania Context triple: [Tycho Brahe, placeOfBirth, Scania]
-
A.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
B.
Volvo Cars
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
C.
Saab Kockums
Saab Kockums is a Swedish shipyard and defense company best known for designing and building advanced submarines and naval vessels.
-
D.
Saab Automobile
Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
-
E.
Saab AB
Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
- 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: Scania Triple: [Tycho Brahe, placeOfBirth, Scania]
Generated description
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Scania Target entity description: Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
-
A.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
B.
Volvo Cars
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
C.
Saab Kockums
Saab Kockums is a Swedish shipyard and defense company best known for designing and building advanced submarines and naval vessels.
-
D.
Saab Automobile
Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
-
E.
Saab AB
Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
- 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8f531f481909a40558811379992 |
completed | March 1, 2026, 10:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7f2b76308190a843ccad1ef95f07 |
completed | March 7, 2026, 7:40 p.m. |
| NEDg | Description generation | batch_69ac7fba00f0819086a0fffa090c5809 |
completed | March 7, 2026, 7:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac807ead9c819088f7195aec87a538 |
completed | March 7, 2026, 7:46 p.m. |
Created at: March 1, 2026, 7:42 p.m.