Triple
T883936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of London |
E19087
|
entity |
| Predicate | daytimePopulation |
P20734
|
FINISHED |
| Object | hundreds of thousands of commuters |
—
|
LITERAL 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: hundreds of thousands of commuters | Statement: [City of London, daytimePopulation, hundreds of thousands of commuters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: daytimePopulation Context triple: [City of London, daytimePopulation, hundreds of thousands of commuters]
-
A.
metropolitanAreaPopulationApproximate
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
-
B.
majorPopulationIn
Indicates that a significant portion of a population is located in or primarily associated with a particular place or region.
-
C.
hasPopulationAsOf
Indicates that a population count is associated with a specific point or date in time when that population figure was valid or recorded.
-
D.
populationIncludes
Indicates that a population contains or encompasses the specified individual(s) or subgroup(s) as members or elements.
-
E.
population
Indicates the total number of individuals living in or present within a specified area or group.
- F. None of above. chosen
Provenance (4 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ace495348190aec66f35ea90bc89 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8eca748190b58e0f08b30fba43 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4a38ec8190915916d80299ab55 |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:39 p.m.