Triple
T32149741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Munch |
E821124
|
entity |
| Predicate | portrayedForYears |
P124245
|
FINISHED |
| Object | 1993–2016 |
—
|
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: 1993–2016 | Statement: [John Munch, portrayedForYears, 1993–2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedForYears Context triple: [John Munch, portrayedForYears, 1993–2016]
-
A.
portrayalDurationYears
chosen
Indicates the number of years an entity has continuously portrayed or represented another entity or role.
-
B.
portrayalEndYear
Indicates the year in which a particular portrayal of an entity (such as a role, character, or representation) concluded.
-
C.
portrayedVia
Indicates that one entity is represented, depicted, or expressed through a particular medium, method, or channel.
-
D.
characterPortrayedInYear
Indicates that a particular character was portrayed in a specific year.
-
E.
notablePortrayalPeriod
Indicates the time period during which a particular portrayal or depiction of something or someone is especially recognized or notable.
- 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_69f3490520d081909b2f1271dab75faa |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe2f078c24819082ba396b56f02808 |
completed | May 8, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69fe228fe1988190baf3bb34897f3dbe |
completed | May 8, 2026, 5:51 p.m. |
Created at: May 1, 2026, 12:31 a.m.