Triple
T19819503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Strand theatres |
E476147
|
entity |
| Predicate | culturalRegion |
P1968
|
FINISHED |
| Object | Theatreland |
—
|
NE NERFINISHED |
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: Theatreland | Statement: [The Strand theatres, culturalRegion, Theatreland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theatreland Context triple: [The Strand theatres, culturalRegion, Theatreland]
-
A.
Theatreland
chosen
Theatreland is the famous concentration of major commercial theatres in London’s West End, known for its long-running plays and musicals.
-
B.
London Theatreland
London Theatreland is the main theatre district in central London, renowned for its concentration of West End theatres staging major commercial plays and musicals.
-
C.
Westend
Westend is a prominent and affluent district in Frankfurt am Main, Germany, known for its elegant residential areas and concentration of banks and corporate offices.
-
D.
Westend
Westend is a residential and commercial locality in Berlin known for its affluent neighborhoods, green spaces, and proximity to the Olympic Stadium.
-
E.
Londiani
Londiani is a town in Kenya’s Rift Valley region, known as a local commercial and transport hub within Kericho County.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654fe0ff8819084bad251b76eff77 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.