Triple
T16721810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Nathan-Turner |
E406364
|
entity |
| Predicate | activeYearsInProfession |
P18004
|
FINISHED |
| Object | 1960s–1990s |
—
|
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: 1960s–1990s | Statement: [John Nathan-Turner, activeYearsInProfession, 1960s–1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInProfession Context triple: [John Nathan-Turner, activeYearsInProfession, 1960s–1990s]
-
A.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
B.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
C.
workYear
Indicates the specific year or span of years during which an entity (such as a person or organization) was engaged in work or employment.
-
D.
activeYearsInSport
Indicates the span of years during which an entity actively participated in a particular sport.
-
E.
activeYearsPeak
Indicates the span of years during which an entity was at the height of its activity or prominence.
- 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_69d8838f242881908abd8bc138795886 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e38743750c81908a980372ad8f1a6a |
completed | April 18, 2026, 1:29 p.m. |
| PD | Predicate disambiguation | batch_69e319c379f88190ac0adf812486f598 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:20 a.m.