Triple
T28788242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ana Lúcia |
E726875
|
entity |
| Predicate | notableBearerOfRole |
P203466
|
FINISHED |
| Object | Aurelia in the film "Love Actually" |
—
|
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: Aurelia in the film "Love Actually" | Statement: [Ana Lúcia, notableBearerOfRole, Aurelia in the film "Love Actually"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableBearerOfRole Context triple: [Ana Lúcia, notableBearerOfRole, Aurelia in the film "Love Actually"]
-
A.
notableHolderRole
Indicates that an entity is recognized for holding a particular role, office, or position in a notable or distinguished capacity.
-
B.
notableRecipientRole
Indicates that an entity has received a notable award, honor, or recognition specifically in the capacity of a particular role or position.
-
C.
notableFormerHolderRole
Indicates that an entity previously held a particular notable role or position.
-
D.
notableOfficerRole
Indicates that an entity holds or has held a particularly significant or distinguished officer position within an organization or institution.
-
E.
notableCountryRole
Indicates that an entity holds a significant or prominent role, position, or function within a particular country.
- 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_69f0319aabec81908368720196f69a35 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_6a0189f3f0248190a1b018164d18e6f5 |
completed | May 11, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_6a0187ee0920819097047bb55e1f9506 |
completed | May 11, 2026, 7:40 a.m. |
| PDg | Predicate description generation | batch_6a0189f318c881909c4995203a58a097 |
completed | May 11, 2026, 7:49 a.m. |
Created at: April 28, 2026, 6:22 a.m.