Triple
T12869683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liv Freundlich |
E307814
|
entity |
| Predicate | hasPhotographWith |
P12849
|
FINISHED |
| Object | Julianne Moore at fashion events |
—
|
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: Julianne Moore at fashion events | Statement: [Liv Freundlich, hasPhotographWith, Julianne Moore at fashion events]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotographWith Context triple: [Liv Freundlich, hasPhotographWith, Julianne Moore at fashion events]
-
A.
hasPhotographBy
Indicates that an entity is depicted in or associated with a photograph that was created or taken by a specified photographer.
-
B.
hasPhotograph
chosen
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
C.
hasPhotographs
Indicates that one entity possesses, contains, or is associated with one or more photographs of another entity or subject.
-
D.
hasPhotoOn
Indicates that one entity has an associated photograph stored, displayed, or linked on another entity (such as a platform, page, or medium).
-
E.
hasPhotographicRecordSince
Indicates that a photographic record of an entity has existed continuously since a specified point in time.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:38 p.m.