Triple
T30100515
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carpenders Park |
E764978
|
entity |
| Predicate | commuterAreaFor |
P13163
|
FINISHED |
| Object | London |
—
|
NE NERFINISHED |
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: London | Statement: [Carpenders Park, commuterAreaFor, London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commuterAreaFor Context triple: [Carpenders Park, commuterAreaFor, London]
-
A.
commuterHubFor
Indicates a location that serves as a primary transit or gathering point for commuters traveling to or from another place.
-
B.
commuterCorridorFor
Indicates a route or area that serves as a primary pathway for regular travel between two locations, typically used by commuters.
-
C.
commuterDestination
Indicates that a location serves as the endpoint or target place to which a person regularly travels for commuting.
-
D.
commuterMarket
Indicates a market or customer segment composed primarily of people who regularly commute, typically targeted based on their commuting patterns and needs.
-
E.
isCommuterRegionFor
chosen
Indicates that one region primarily serves as a residential base whose inhabitants regularly travel to another region for work or daily activities.
- F. None of above.
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_69f22474e4288190b5f895fe3974aa92 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6a916d2e08190bafc01cba73b6469 |
completed | May 3, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69f6a7548eb48190a69b60a3c6ad53b9 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 29, 2026, 7:08 p.m.