Triple
T27782691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cafe 80's |
E699374
|
entity |
| Predicate | diegeticYearOfOperation |
P82414
|
FINISHED |
| Object | 2015 |
—
|
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: 2015 | Statement: [Cafe 80's, diegeticYearOfOperation, 2015]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diegeticYearOfOperation Context triple: [Cafe 80's, diegeticYearOfOperation, 2015]
-
A.
operatedInYear
chosen
Indicates that an entity was actively operating or functioning during a specified year.
-
B.
operatedDuring
Indicates that an action, process, or system was functioning or in operation throughout a specified time period or event.
-
C.
deploymentYear
Indicates the calendar year in which an entity (such as a system, product, or resource) was first put into active use or operation.
-
D.
operatedYearRound
Indicates that the subject entity functioned or was in operation continuously throughout the entire year, without seasonal closure.
-
E.
describedYear
Indicates the specific year in which something is described, documented, or characterized.
- 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_69ef6a4b5a9081909c9111396c2be3d2 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f637cf6d248190a86a85cfeba3719b |
completed | May 2, 2026, 5:43 p.m. |
| PD | Predicate disambiguation | batch_69f6318ae6f08190b3f85f9201046a15 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 27, 2026, 5:11 p.m.