Triple
T16244255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dane DeHaan |
E394328
|
entity |
| Predicate | workedWith |
P398
|
FINISHED |
| Object | Cara Delevingne |
E268541
|
NE 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: Cara Delevingne | Statement: [Dane DeHaan, workedWith, Cara Delevingne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cara Delevingne Context triple: [Dane DeHaan, workedWith, Cara Delevingne]
-
A.
Cara Delevingne
chosen
Cara Delevingne is an English model-turned-actress known for her distinctive eyebrows, high-fashion runway work, and prominent film roles in projects like "Paper Towns" and "Suicide Squad."
-
B.
Poppy Delevingne
Poppy Delevingne is a British model, socialite, and actress known for her work with major fashion brands and her prominent role in London’s fashion and social scenes.
-
C.
Rosie Huntington-Whiteley
Rosie Huntington-Whiteley is an English model and actress known for her work with Victoria’s Secret and for film roles including her appearance in Mad Max: Fury Road.
-
D.
Suki Waterhouse
Suki Waterhouse is an English model, actress, and singer known for her fashion work, film roles, and music career.
-
E.
Bella Hadid
Bella Hadid is an American fashion model known for her work with major luxury brands and frequent appearances on international Vogue covers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24560c6848190ae0e85ecb11a9264 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ee135b881909cc1b6919bc7af29 |
completed | May 10, 2026, 4:51 a.m. |
Created at: April 10, 2026, 5:04 a.m.