Triple
T14379348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Norwood |
E356558
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Penge |
E47020
|
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: Penge | Statement: [South Norwood, adjacentTo, Penge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penge Context triple: [South Norwood, adjacentTo, Penge]
-
A.
Penge
chosen
Penge is a suburban district in southeast London known for its Victorian architecture and proximity to Crystal Palace.
-
B.
Pengo
Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
-
C.
Penkun
Penkun is a small historic town in northeastern Germany, located near the Polish border in the state of Mecklenburg-Vorpommern.
-
D.
Pangim
Pangim, also known as Panaji, is the riverside city that serves as the administrative and cultural center of the Indian state of Goa.
-
E.
Peng
Peng is a Chinese surname borne by numerous notable figures in politics, arts, and academia throughout Chinese history and the modern era.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900a67e08190ab1dcf36e6bb3405 |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c590660819090652e75418f2747 |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:16 a.m.