Triple
T19011256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telford Shopping Centre |
E465229
|
entity |
| Predicate | hasZoning |
P727
|
FINISHED |
| Object | multiple themed malls (e.g., Fashion Quarter, Southern Quarter, etc., depending on branding phase) |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: multiple themed malls (e.g., Fashion Quarter, Southern Quarter, etc., depending on branding phase) | Statement: [Telford Shopping Centre, hasZoning, multiple themed malls (e.g., Fashion Quarter, Southern Quarter, etc., depending on branding phase)]
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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6a8f99081908cf535a4ec11adf7 |
completed | April 20, 2026, 7:32 a.m. |
Created at: April 10, 2026, 12:02 p.m.