Triple
T11694359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago (1927 film) |
E277952
|
entity |
| Predicate | followsPlot |
P101302
|
FINISHED |
| Object | woman gains notoriety after being accused of murder |
—
|
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: woman gains notoriety after being accused of murder | Statement: [Chicago (1927 film), followsPlot, woman gains notoriety after being accused of murder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: followsPlot Context triple: [Chicago (1927 film), followsPlot, woman gains notoriety after being accused of murder]
-
A.
follows
Indicates that one entity comes after, moves behind, or acts in accordance with another entity in time, space, or sequence.
-
B.
followsSeries
Indicates that one entity comes after another as the next installment or continuation within an ordered series.
-
C.
followsEpisode
Indicates that one episode occurs directly after another in a sequence or series.
-
D.
followsStoryOf
Indicates that one narrative, account, or storyline continues from, is based on, or is derived from the events or structure of another.
-
E.
followsInFilmography
Indicates that one work in a person’s filmography comes after another in chronological or credited order.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a47b9eb48190976a35e91e25b56b |
completed | April 10, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69d88a7b30948190b616a9db5c5488d5 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d89546a8688190b51455b5e12caf91 |
completed | April 10, 2026, 6:14 a.m. |
Created at: April 8, 2026, 9:40 p.m.