Triple
T13828422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Street |
E332314
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Tech City |
E925604
|
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: Tech City | Statement: [Old Street, partOf, Tech City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tech City Context triple: [Old Street, partOf, Tech City]
-
A.
Tech City
chosen
Tech City is a major technology startup and innovation hub centered around East London’s Silicon Roundabout.
-
B.
Silicon Fen
Silicon Fen is the high-tech business and research cluster around Cambridge, England, known for its concentration of technology, software, and biotech companies.
-
C.
City Tech
City Tech is a public college in Brooklyn, New York, specializing in technology, engineering, and professional studies as part of the City University of New York (CUNY) system.
-
D.
Cidade da Tecnologia
Cidade da Tecnologia is a nickname for Campina Grande, a Brazilian city renowned as a major regional hub for technology, innovation, and higher education.
-
E.
Cybercity Magarpatta
Cybercity Magarpatta is the major IT and business hub within Magarpatta City in Pune, India, housing numerous technology companies and corporate offices.
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02970df88190a1bf35dffd131d9d |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8ea22c081909cc34f1030a8589b |
completed | May 3, 2026, 9:06 p.m. |
Created at: April 9, 2026, 10:13 p.m.