Triple
T14620522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burnie Dockers Football Club |
E343205
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Dockers
Dockers is a popular global clothing brand best known for its casual khaki pants and business-casual apparel.
|
E1111068
|
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: Dockers | Statement: [Burnie Dockers Football Club, nickname, Dockers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dockers Context triple: [Burnie Dockers Football Club, nickname, Dockers]
-
A.
Dockers
Dockers is the common nickname for the Fremantle Dockers, an Australian Football League (AFL) club based in Fremantle, Western Australia.
-
B.
Skopeo
Skopeo is a command-line utility for inspecting, copying, and managing container images across different container registries without requiring a local container engine.
-
C.
Dockery
Dockery is an English surname most notably associated with actress Michelle Dockery, known for her role in the television series "Downton Abbey."
-
D.
Docker
Docker is an open-source platform that uses containerization to package, distribute, and run applications consistently across different computing environments.
-
E.
Above the Dock
"Above the Dock" is a brief imagist poem by T. E. Hulme that vividly captures a nighttime urban scene through precise, economical imagery.
- 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: Dockers Triple: [Burnie Dockers Football Club, nickname, Dockers]
Generated description
Dockers is a popular global clothing brand best known for its casual khaki pants and business-casual apparel.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dockers Target entity description: Dockers is a popular global clothing brand best known for its casual khaki pants and business-casual apparel.
-
A.
Dockers
Dockers is the common nickname for the Fremantle Dockers, an Australian Football League (AFL) club based in Fremantle, Western Australia.
-
B.
Skopeo
Skopeo is a command-line utility for inspecting, copying, and managing container images across different container registries without requiring a local container engine.
-
C.
Dockery
Dockery is an English surname most notably associated with actress Michelle Dockery, known for her role in the television series "Downton Abbey."
-
D.
Docker
Docker is an open-source platform that uses containerization to package, distribute, and run applications consistently across different computing environments.
-
E.
Above the Dock
"Above the Dock" is a brief imagist poem by T. E. Hulme that vividly captures a nighttime urban scene through precise, economical imagery.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb466a61c81908a110d40fb959b6f |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda92688a08190bb27434acc7c12e7 |
completed | May 8, 2026, 9:13 a.m. |
| NEDg | Description generation | batch_69fdb2994f2c8190960bd8a27e303cdb |
completed | May 8, 2026, 9:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdb40ee364819087c371fa1e25506c |
completed | May 8, 2026, 9:59 a.m. |
Created at: April 10, 2026, 1:25 a.m.