Triple
T8450054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Turner’s Come and Gone |
E199776
|
entity |
| Predicate | chronologicalPositionInPittsburghCycle |
P83431
|
FINISHED |
| Object | set in the 1910s |
—
|
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: set in the 1910s | Statement: [Joe Turner’s Come and Gone, chronologicalPositionInPittsburghCycle, set in the 1910s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chronologicalPositionInPittsburghCycle Context triple: [Joe Turner’s Come and Gone, chronologicalPositionInPittsburghCycle, set in the 1910s]
-
A.
chronologicalPosition
Indicates the relative ordering of one event or entity in time with respect to another.
-
B.
chronologicalPositionInLabors
Indicates the specific order or sequence in which a given labor occurs within a defined series of labors.
-
C.
occursInYearsOfCycle
Indicates that an event or phenomenon takes place during specific years within a recurring multi-year cycle.
-
D.
chronologicalStatus
Indicates the temporal state or phase of something relative to a timeline or sequence of events.
-
E.
positionInEra
Indicates the temporal placement or role of something within a specific historical or chronological era.
- 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_69ca8318231881908fd1bc1c4d45d286 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe44707b88190b3d8b30c45ef4496 |
completed | March 31, 2026, 3:12 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:09 p.m.