Triple
T7075897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Johnson |
E164816
|
entity |
| Predicate | numberOfKnownRecordings |
P74853
|
FINISHED |
| Object | 29 |
—
|
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: 29 | Statement: [Robert Johnson, numberOfKnownRecordings, 29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfKnownRecordings Context triple: [Robert Johnson, numberOfKnownRecordings, 29]
-
A.
typicalNumberOfRecordingsChosen
Indicates the usual or standard number of recordings that are selected in a given context or process.
-
B.
numberOfSongs
Indicates the quantity of songs associated with a given entity.
-
C.
numberOfKnownPlays
Indicates the total count of plays that are known to be associated with a given entity (such as an author, theater, or period).
-
D.
numberOfTracks
Indicates the quantity of tracks associated with a given entity.
-
E.
recordingOf
Indicates that one entity is an audio or video capture or performance that documents, represents, or preserves another entity (such as a work, event, or expression).
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ce3d3c81908cbb912b256aadbf |
completed | March 27, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bfcb948190a5ada74fb8c054cb |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:40 p.m.