Triple
T10759851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Michael Murphy |
E253795
|
entity |
| Predicate | timePeriodOfSeries |
P302
|
FINISHED |
| Object | early 1980s |
—
|
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: early 1980s | Statement: [John Michael Murphy, timePeriodOfSeries, early 1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timePeriodOfSeries Context triple: [John Michael Murphy, timePeriodOfSeries, early 1980s]
-
A.
timeInSeries
Indicates that one temporal value occurs within the duration or sequence span defined by another in a series.
-
B.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
C.
timePeriodAnalyzed
Indicates that a specified time period is the focus or scope of an analysis or evaluation.
-
D.
timePeriodModeled
Indicates that a given time period is represented, simulated, or otherwise captured within a particular model or modeling framework.
-
E.
timeIntervalLength
Indicates the duration or length of a specified time interval.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d72ea21c5081908babc049d0330a75 |
completed | April 9, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69d6f311529c819080ca5493d55d6050 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:16 p.m.