Triple
T31530699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie-Octobre |
E804469
|
entity |
| Predicate | characterPlayedBy_Danielle Darrieux |
P197171
|
FINISHED |
| Object | Marie-Hélène Dumoulin |
—
|
NE NERFINISHED |
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: Marie-Hélène Dumoulin | Statement: [Marie-Octobre, characterPlayedBy_Danielle Darrieux, Marie-Hélène Dumoulin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPlayedBy_Danielle Darrieux Context triple: [Marie-Octobre, characterPlayedBy_Danielle Darrieux, Marie-Hélène Dumoulin]
-
A.
characterPlayedBy Emmanuelle Chriqui
Indicates that the role or character in question is portrayed or acted by Emmanuelle Chriqui.
-
B.
hasBrigitteBardotRoleType
Indicates that an entity has a role type specifically associated with Brigitte Bardot (e.g., portraying her or a role category defined in relation to her).
-
C.
hasMarleneDietrichRoleType
Indicates that an entity has a specific type or category of role associated with Marlene Dietrich.
-
D.
characterPortrayedByGloriaSwanson
Indicates that a character is portrayed or played by Gloria Swanson.
-
E.
characterPlayedByClaudetteColbert
Indicates that a given character is portrayed or acted by Claudette Colbert.
- 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69fe7b1b3460819081172731b52ac15b |
completed | May 9, 2026, 12:08 a.m. |
Created at: April 30, 2026, 10:01 p.m.