Triple
T36479744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank (Black Mirror: Hang the DJ) |
E898777
|
entity |
| Predicate | simulationCount |
P79164
|
FINISHED |
| Object | 1 of 1000 simulations |
—
|
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: 1 of 1000 simulations | Statement: [Frank (Black Mirror: Hang the DJ), simulationCount, 1 of 1000 simulations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: simulationCount Context triple: [Frank (Black Mirror: Hang the DJ), simulationCount, 1 of 1000 simulations]
-
A.
simCount
Indicates the number of times two entities or items are considered similar according to a defined similarity measure.
-
B.
sampleNumber
Indicates that an entity is identified or associated with a specific sample number within a set of samples.
-
C.
trialNumber
Indicates the ordinal position or sequence index of a specific trial within a series of trials.
-
D.
numberOfExperiments
chosen
Indicates the total count of experiments associated with or performed in a given context or entity.
-
E.
numberOfExecutions
Indicates the count of times a particular action, process, or event has been carried out.
- 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_69f76e5a0e088190a2b6706aeb41723c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.