Triple
T34476368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Zelig |
E885047
|
entity |
| Predicate | featuredAlongside |
P25756
|
FINISHED |
| Object | doctored archival footage of historical figures |
—
|
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: doctored archival footage of historical figures | Statement: [Leonard Zelig, featuredAlongside, doctored archival footage of historical figures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuredAlongside Context triple: [Leonard Zelig, featuredAlongside, doctored archival footage of historical figures]
-
A.
alsoFeatured
Indicates that an entity appears in addition to another primary entity within the same context, work, or presentation.
-
B.
appearsAlongside
chosen
Indicates that two entities are present or occur together in the same context, setting, or instance.
-
C.
featuredIn
Indicates that one entity appears or is prominently included within another entity, such as a person, work, or item being showcased in a larger work, event, or context.
-
D.
featuredOn
Indicates that one entity is prominently presented, highlighted, or showcased on or within another entity (such as a platform, publication, or product).
-
E.
laterFeaturedIn
Indicates that an entity was featured or highlighted at a later time in another work, context, or medium.
- 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_69f349c880408190ade571c471ab154a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0332a08b48819094aaed6e04a36886 |
completed | May 12, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_6a0331998a688190b5d919697d4231ac |
completed | May 12, 2026, 1:56 p.m. |
Created at: May 1, 2026, 2:01 a.m.