Triple
T13042473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent House railway station |
E327227
|
entity |
| Predicate | nearbyArea |
P2064
|
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: [Kent House railway station, nearbyArea, Penge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penge Context triple: [Kent House railway station, nearbyArea, 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.
Pangim
Pangim, also known as Panaji, is the riverside city that serves as the administrative and cultural center of the Indian state of Goa.
-
D.
Peng
Peng is a Chinese surname borne by numerous notable figures in politics, arts, and academia throughout Chinese history and the modern era.
-
E.
Pang
Pang is a variant transliteration of the Chinese surname commonly romanized as Peng.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804f0318819081516e2ca1de6797 |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbd5139c8190aaec6487f074f251 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:56 p.m.