Triple
T36324659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris, je t'aime segment "Père-Lachaise" |
E894427
|
entity |
| Predicate | partOfFilmAnthologyStructure |
P202915
|
FINISHED |
| Object | 18 short segments |
—
|
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: 18 short segments | Statement: [Paris, je t'aime segment "Père-Lachaise", partOfFilmAnthologyStructure, 18 short segments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfFilmAnthologyStructure Context triple: [Paris, je t'aime segment "Père-Lachaise", partOfFilmAnthologyStructure, 18 short segments]
-
A.
hasPartInFilmSeries
Indicates that an entity participates in or contributes to one or more installments within a specific film series.
-
B.
distributorOfAnthologyFilm
Indicates that an entity serves as the distributor responsible for releasing or circulating an anthology film.
-
C.
partOfFilmMarketing
Indicates that something is an element or activity belonging to the marketing or promotional campaign for a film.
-
D.
filmWithinFilmTitle
Indicates that a title refers to a fictional film that appears within another (primary) film.
-
E.
filmWithinFilm
Indicates that one film is depicted, referenced, or shown as existing within the narrative of another film.
- 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_69f76e4dcf088190a6c3216c209cab52 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a00cda99c908190980e0bf54cb2e2a4 |
completed | May 10, 2026, 6:25 p.m. |
| PD | Predicate disambiguation | batch_6a00cd1635b08190a791ecfcf87a1d54 |
completed | May 10, 2026, 6:23 p.m. |
| PDg | Predicate description generation | batch_6a00cda8ed348190950ee34bb24ae65b |
completed | May 10, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:09 p.m.