Triple
T21870904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French sector of Wedding |
E539998
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Wedding |
—
|
NE NERFINISHED |
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: Wedding | Statement: [French sector of Wedding, partOf, Wedding]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wedding Context triple: [French sector of Wedding, partOf, Wedding]
-
A.
Wedding
chosen
Wedding is a district in Berlin, Germany, known for its multicultural character and urban residential neighborhoods.
-
B.
Wedding Day
"Wedding Day" is a notable poem by Harlem Renaissance writer and artist Gwendolyn Bennett, reflecting her lyrical style and exploration of Black identity and emotional experience.
-
C.
Wedding Day
"Wedding Day" is a song by Tori Amos from her 2014 studio album *Unrepentant Geraldines*.
-
D.
Wedding Day
"Wedding Day" is a short story by Ernest Hemingway that follows his recurring character Nick Adams through the emotional complexities surrounding marriage and personal relationships.
-
E.
Matrimony
Matrimony is the Christian sacrament in which a man and a woman enter into a lifelong, covenantal union ordered toward the good of the spouses and the procreation and education of children.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f33509d08190b33775abb84d5255 |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:57 p.m.