Triple
T38133829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metroland |
E952293
|
entity |
| Predicate | containsFlashbacksTo |
P169512
|
FINISHED |
| Object | 1960s Paris |
—
|
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: 1960s Paris | Statement: [Metroland, containsFlashbacksTo, 1960s Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsFlashbacksTo Context triple: [Metroland, containsFlashbacksTo, 1960s Paris]
-
A.
portrayedInFlashbacks
Indicates that an entity appears or is depicted specifically within flashback scenes of a narrative work.
-
B.
hasFlashbackStorylines
Indicates that the narrative includes scenes or sequences set in earlier time periods that reveal past events related to the main storyline.
-
C.
featuresFlashback
chosen
Indicates that an event, scene, or narrative segment includes a flashback to an earlier time or prior events.
-
D.
retainsMemoriesOf
Indicates that one entity continues to hold or preserve memories about another entity or about events involving that entity.
-
E.
flashbackStructure
Indicates a narrative relationship where events are presented out of chronological order by returning to earlier moments in time (flashbacks) to inform or reframe the current storyline.
- 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_69f76f083548819082bd2bbf53c79e8e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a01f4d954e08190aff3756955212d67 |
completed | May 11, 2026, 3:25 p.m. |
| PD | Predicate disambiguation | batch_6a01edadb9248190be592287530740a5 |
completed | May 11, 2026, 2:54 p.m. |
Created at: May 3, 2026, 4:21 p.m.