Triple
T5853076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Claire of Belgium |
E130083
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Claire |
E97238
|
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: Claire | Statement: [Princess Claire of Belgium, givenName, Claire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claire Context triple: [Princess Claire of Belgium, givenName, Claire]
-
A.
Claire
chosen
Claire is a feminine given name of French origin meaning "clear" or "bright," commonly used in English-speaking countries.
-
B.
Claire
Claire is a sharp-tongued, alcoholic sister whose acerbic wit and emotional volatility provide both dark humor and tension in Edward Albee’s play "A Delicate Balance."
-
C.
Claire Louise
Claire Louise was the wife of American film actor Robert Armstrong, best known for his role in the classic movie "King Kong."
-
D.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
E.
Claire Kincaid
Claire Kincaid is a fictional assistant district attorney on the television series "Law & Order," known for her idealism and moral conviction in prosecuting cases.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0355038008190bf38980349b533e2 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1b8f8508190942ce1725884d254 |
completed | March 23, 2026, 2:13 a.m. |
Created at: March 22, 2026, 3:55 p.m.