Triple
T19816439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Connie Hedegaard |
E476069
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Connie |
—
|
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: Connie | Statement: [Connie Hedegaard, givenName, Connie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Connie Context triple: [Connie Hedegaard, givenName, Connie]
-
A.
Connie
Connie is the nickname of Connie Hawkins, a legendary American basketball player known for his high-flying, acrobatic style and Hall of Fame career.
-
B.
Connie
Connie is a character associated with Silky the Fairy, likely appearing in the same children’s fantasy setting as her friend.
-
C.
Connie
chosen
Connie is a feminine given name commonly used in English-speaking countries, often as a diminutive of Constance or Concepcion.
-
D.
Connie
Connie is a central character in the romantic drama film "Jack Goes Boating," serving as the shy, soft-spoken love interest whose relationship with Jack drives much of the story’s emotional development.
-
E.
Connie
Connie is the main character in the story "Connie Goes Home," around whom the narrative and its events revolve.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654f9c5b08190987237f5144c3b37 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.