Triple
T1193386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Motherwell |
E25612
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Steelopolis
Steelopolis is a nickname for the Scottish town of Motherwell, historically known for its major steel production industry.
|
E137193
|
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: Steelopolis | Statement: [Motherwell, nickname, Steelopolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steelopolis Context triple: [Motherwell, nickname, Steelopolis]
-
A.
Tinopolis
Tinopolis is the nickname of the Welsh town of Llanelli, historically renowned for its large tinplate industry.
-
B.
Copperopolis
Copperopolis is a historical nickname for the Welsh city of Swansea, reflecting its past prominence as a major center of copper smelting and industry.
-
C.
Spindle City
Spindle City is a historic industrial nickname for Lowell, Massachusetts, reflecting its prominence as a major 19th-century textile manufacturing center.
-
D.
Steel City
Steel City is a nickname for Lorain, Ohio, reflecting its historic role as a major center of steel production and heavy industry.
-
E.
Steel City
Steel City is the industrial nickname for Pueblo, Colorado, reflecting its historic role as a major steel-producing center in the United States.
- 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: Steelopolis Triple: [Motherwell, nickname, Steelopolis]
Generated description
Steelopolis is a nickname for the Scottish town of Motherwell, historically known for its major steel production industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steelopolis Target entity description: Steelopolis is a nickname for the Scottish town of Motherwell, historically known for its major steel production industry.
-
A.
Tinopolis
Tinopolis is the nickname of the Welsh town of Llanelli, historically renowned for its large tinplate industry.
-
B.
Copperopolis
Copperopolis is a historical nickname for the Welsh city of Swansea, reflecting its past prominence as a major center of copper smelting and industry.
-
C.
Spindle City
Spindle City is a historic industrial nickname for Lowell, Massachusetts, reflecting its prominence as a major 19th-century textile manufacturing center.
-
D.
Steel City
Steel City is a nickname for Lorain, Ohio, reflecting its historic role as a major center of steel production and heavy industry.
-
E.
Steel City
Steel City is the industrial nickname for Pueblo, Colorado, reflecting its historic role as a major steel-producing center in the United States.
- 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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd7743548190a70d3f3c7378aaa7 |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7650de3c8190b2c246436a3d25b1 |
completed | March 7, 2026, 7:02 p.m. |
| NEDg | Description generation | batch_69ac76e2df308190807cc6b7d7555e69 |
completed | March 7, 2026, 7:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac77585c708190b5f4b239d9574cd7 |
completed | March 7, 2026, 7:07 p.m. |
Created at: March 1, 2026, 7:46 p.m.