Triple
T1201059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wedding March |
E25781
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Cecelia |
E135814
|
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: Cecelia | Statement: [The Wedding March, featuresCharacter, Cecelia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cecelia Context triple: [The Wedding March, featuresCharacter, Cecelia]
-
A.
Cecilia
chosen
Cecilia is a feminine given name of Latin origin, traditionally associated with Saint Cecilia, the patron saint of music.
-
B.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
C.
Doña Sol
Doña Sol is a seductive and aristocratic woman who becomes the torero’s dangerous love interest in the 1922 silent film "Blood and Sand."
-
D.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
-
E.
Clementina
Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
- 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_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd9ec3488190afe35af54efae5e9 |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac831703bc8190839deb02075cb8fd |
completed | March 7, 2026, 7:57 p.m. |
Created at: March 1, 2026, 7:46 p.m.