Triple
T1795261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infamous (2006 film) |
E39588
|
entity |
| Predicate | runtimeApprox |
P31999
|
FINISHED |
| Object | 110 minutes |
—
|
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: 110 minutes | Statement: [Infamous (2006 film), runtimeApprox, 110 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runtimeApprox Context triple: [Infamous (2006 film), runtimeApprox, 110 minutes]
-
A.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
B.
approximateSize
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
-
C.
runtimeMinutes
Indicates the total duration of something, typically a media work or process, measured in minutes.
-
D.
approximateCapacity
Indicates that one entity has an estimated or rough capacity value relative to another or to a specified measure.
-
E.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab61b6ea188190aab9fb839bf1e367 |
completed | March 6, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69aa61d2f7a8819090301f92d3e358c7 |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab61b5c8988190bb2b46182a4eb5b4 |
completed | March 6, 2026, 11:22 p.m. |
Created at: March 4, 2026, 7:32 p.m.