Triple
T19896309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro (British newspaper) |
E478160
|
entity |
| Predicate | typicalReadingContext |
P94265
|
FINISHED |
| Object | public transport commuting |
—
|
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: public transport commuting | Statement: [Metro (British newspaper), typicalReadingContext, public transport commuting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalReadingContext Context triple: [Metro (British newspaper), typicalReadingContext, public transport commuting]
-
A.
readingContext
chosen
Indicates the situational or surrounding information (such as location, time, or medium) within which a reading activity or act of reading takes place.
-
B.
readingAid
Indicates that one entity assists or facilitates another entity’s ability to read or engage in reading activities.
-
C.
typicalLanguageOfReadings
Indicates the language that is most commonly used for readings or interpretations associated with a given entity.
-
D.
containsReading
Indicates that one entity includes or encompasses a particular reading (such as a measurement, value, or interpretation) within it.
-
E.
reading
Indicates that an entity is engaged in the activity of interpreting and understanding written or printed material from another entity or source.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593dba78819082c8b80e65246171 |
completed | April 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:52 p.m.