Triple
T8155602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Celine Buckens |
E190441
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Celine Buckens |
E190441
|
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: Celine Buckens | Statement: [Celine Buckens, name, Celine Buckens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Celine Buckens Context triple: [Celine Buckens, name, Celine Buckens]
-
A.
Celine Buckens
chosen
Celine Buckens is a Belgian-born British actress best known for her breakout role in Steven Spielberg’s film "War Horse" and subsequent work in television dramas.
-
B.
Sophie Wilmès
Sophie Wilmès is a Belgian liberal politician who became the country’s first female prime minister, leading the federal government during the initial phase of the COVID-19 pandemic.
-
C.
Eeltije Vinck
Eeltije Vinck was the wife of Dutch Golden Age landscape painter Meindert Hobbema.
-
D.
Christine Defraigne
Christine Defraigne is a Belgian liberal politician who has served in prominent national roles, including as President of the Senate.
-
E.
Christine Leunens
Christine Leunens is a New Zealand–based Belgian-American novelist best known for her book "Caging Skies," which was adapted into the Oscar-winning film "Jojo Rabbit."
- 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_69ca82bfeb6481909d07b91b5cf69f59 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb44d725b88190b77dc7537c1fa95d |
completed | March 31, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf0f68c88190be9aab03de6bf4a0 |
completed | April 1, 2026, 6:45 a.m. |
Created at: March 30, 2026, 5:37 p.m.