Triple
T1257684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Celine Dion |
E12428
|
entity |
| Predicate | residencyShow |
P24949
|
FINISHED |
| Object | Celine |
E143077
|
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 | Statement: [Celine Dion, residencyShow, Celine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Celine Context triple: [Celine Dion, residencyShow, Celine]
-
A.
Céline
chosen
Céline is the French given name of internationally renowned Canadian singer Céline Dion.
-
B.
Micheline
Micheline is a feminine given name of French origin, commonly used in French-speaking countries.
-
C.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
D.
Carine
Carine is a feminine given name, often considered a variant of names like Catherine or Karine, used in various European languages.
-
E.
Cecilia
Cecilia is a feminine given name of Latin origin, traditionally associated with Saint Cecilia, the patron saint of music.
- 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bfaa2b508190a3f61c67b3fa3ad4 |
completed | March 1, 2026, 10:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac9980a2a4819094ab26ac390476be |
completed | March 7, 2026, 9:32 p.m. |
Created at: March 1, 2026, 7:50 p.m.