Triple
T2263529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Goose Green |
E50091
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Goose Green |
E74877
|
NE FINISHED |
How this triple was built (2 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: Goose Green | Statement: [Battle of Goose Green, location, Goose Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goose Green Context triple: [Battle of Goose Green, location, Goose Green]
-
A.
Goose Green
chosen
Goose Green is a settlement on East Falkland in the Falkland Islands, best known as the site of a major land battle during the 1982 Falklands War.
-
B.
Goose Green
Goose Green is a small public park and open green space in the East Dulwich area of south London, popular for recreation and community events.
-
C.
Camel Blue
Camel Blue is a popular lighter-tar, filtered cigarette variety in the Camel brand’s product line.
-
D.
Ivy Green
Ivy Green is the historic childhood home of Helen Keller, now preserved as a museum in Tuscumbia, Alabama.
-
E.
Velvet Brown
Velvet Brown is the determined young English girl who dreams of racing her beloved horse to victory in the classic novel and film "National Velvet."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a88b01e0048190ba96431b5f990ba9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc18d7fc08190851765683d1b8092 |
completed | March 7, 2026, 6:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71cfd3b08190988474aa0fa985fe |
completed | March 9, 2026, 7:08 a.m. |
Created at: March 4, 2026, 7:48 p.m.