Triple
T8596443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eskilstuna |
E203558
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Stålstaden
Stålstaden is a Swedish city nickname referring to Eskilstuna’s historic role as a major steel and metalworking industrial center.
|
E746240
|
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: Stålstaden | Statement: [Eskilstuna, nickname, Stålstaden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stålstaden Context triple: [Eskilstuna, nickname, Stålstaden]
-
A.
Ballstad
Ballstad is a fishing village in Norway’s Lofoten archipelago, known for its scenic coastal landscape and traditional maritime culture.
-
B.
Strömstad
Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
-
C.
Hjulsta
Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
-
D.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
E.
Ystad
Ystad is a historic coastal town in southern Sweden known for its medieval architecture and as the setting of Henning Mankell’s Kurt Wallander crime novels.
- 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: Stålstaden Triple: [Eskilstuna, nickname, Stålstaden]
Generated description
Stålstaden is a Swedish city nickname referring to Eskilstuna’s historic role as a major steel and metalworking industrial center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stålstaden Target entity description: Stålstaden is a Swedish city nickname referring to Eskilstuna’s historic role as a major steel and metalworking industrial center.
-
A.
Ballstad
Ballstad is a fishing village in Norway’s Lofoten archipelago, known for its scenic coastal landscape and traditional maritime culture.
-
B.
Strömstad
Strömstad is a coastal town and municipality in western Sweden, near the Norwegian border, known for its archipelago, tourism, and ferry connections.
-
C.
Hjulsta
Hjulsta is a suburb in northwestern Stockholm, Sweden, known for being the terminus of one of the Stockholm metro lines.
-
D.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
E.
Ystad
Ystad is a historic coastal town in southern Sweden known for its medieval architecture and as the setting of Henning Mankell’s Kurt Wallander crime novels.
- 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_69ca832a7f108190b4e4f5648abf4aa2 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46c945dc8190a313c61c0db46187 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea8d3fcfc8190bc51a38715ed453e |
completed | April 2, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69ceac90764c81908c349729bd22a9af |
completed | April 2, 2026, 5:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cead4a4f148190aa39e774528730c9 |
completed | April 2, 2026, 5:54 p.m. |
Created at: March 30, 2026, 6:23 p.m.