Triple
T3075377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beckenham |
E64122
|
entity |
| Predicate | locatedNear |
P294
|
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: [Beckenham, locatedNear, Penge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penge Context triple: [Beckenham, locatedNear, 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.
Peng
Peng is a Chinese surname borne by numerous notable figures in politics, arts, and academia throughout Chinese history and the modern era.
-
D.
Pang
Pang is a variant transliteration of the Chinese surname commonly romanized as Peng.
-
E.
Penipe
Penipe is a small town and canton in central Ecuador known for its agricultural economy and proximity to the active Tungurahua volcano.
- 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_69ad857a8aec8190bfdfd9c14554ac5a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada150d8e08190bde5f68e800e8feb |
completed | March 8, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f889e7fc8190858845221998f321 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:02 p.m.