Triple
T31755503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Ratatouille (restaurant in the film’s ending) |
E810546
|
entity |
| Predicate | frontOfHouse |
P173455
|
FINISHED |
| Object | Alfredo Linguini |
—
|
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: Alfredo Linguini | Statement: [La Ratatouille (restaurant in the film’s ending), frontOfHouse, Alfredo Linguini]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frontOfHouse Context triple: [La Ratatouille (restaurant in the film’s ending), frontOfHouse, Alfredo Linguini]
-
A.
homeFront
Indicates a relationship where an entity is associated with the domestic sphere or civilian side of a conflict, typically supporting war efforts away from the battlefield.
-
B.
frontOf
Indicates that one entity is positioned directly before another along a primary viewing or movement direction.
-
C.
frontIn
Indicates that one entity is positioned directly in front of another entity in space or sequence.
-
D.
frontType
Indicates the type or category of a front (e.g., boundary or leading side) that one entity presents or forms relative to another.
-
E.
hasFrontDesk
Indicates that one entity provides or is equipped with a front desk service or reception area for another entity.
- 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_69f348e340d48190b780fae618c51464 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6b56ed31481908c3e5d749e46bad9 |
completed | May 3, 2026, 2:39 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
| PDg | Predicate description generation | batch_69f6b49339048190b617a6749f648825 |
completed | May 3, 2026, 2:36 a.m. |
Created at: April 30, 2026, 11:29 p.m.