Triple
T32537451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1923 Grouping |
E831626
|
entity |
| Predicate | affectedCompaniesCount |
P17342
|
FINISHED |
| Object | over 100 pre-grouping railway companies |
—
|
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: over 100 pre-grouping railway companies | Statement: [1923 Grouping, affectedCompaniesCount, over 100 pre-grouping railway companies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedCompaniesCount Context triple: [1923 Grouping, affectedCompaniesCount, over 100 pre-grouping railway companies]
-
A.
affectedCompany
chosen
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
B.
estimatedAffectedPeople
Indicates the estimated number of people expected to be impacted by a particular event, condition, or action.
-
C.
affectedPeople
Indicates the people who are impacted or influenced by a particular event, action, or condition.
-
D.
affectedProject
Indicates that one entity has an impact on, or is influenced by, a particular project.
-
E.
numberOfAffectedSettlements
Indicates the count of distinct settlements that are impacted by a particular event, condition, or action.
- 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_69f34924b1cc8190ad3aca0c0f012a7e |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f7516d5b4081908588a6feb541f355 |
completed | May 3, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69f74d40ebb081909daf60623e38f41d |
completed | May 3, 2026, 1:27 p.m. |
Created at: May 1, 2026, 1:01 a.m.