Triple
T14938859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | She Loves Me |
E372467
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | Dear Friend |
E795214
|
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: Dear Friend | Statement: [She Loves Me, notableSong, Dear Friend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dear Friend Context triple: [She Loves Me, notableSong, Dear Friend]
-
A.
Dear Friend
chosen
"Dear Friend" is a song by the American rock band Wild Life.
-
B.
Dear My Friends
Dear My Friends is a South Korean television drama series that poignantly portrays the lives, friendships, and struggles of a group of elderly friends.
-
C.
Dear Love
"Dear Love" is a musical number from the Broadway show "Flora the Red Menace," which marked Liza Minnelli’s Tony-winning debut.
-
D.
Dear Old Friend
"Dear Old Friend" is a musical number from Andrew Lloyd Webber’s stage sequel to The Phantom of the Opera, *Love Never Dies*.
-
E.
Dear John
Dear John is an American sitcom that aired from 1988 to 1992, starring Judd Hirsch as a recently divorced man navigating single life and friendships in a New York City support group.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64904d88190b6b4140da8e8199d |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e9078508190b5cbfe84125ba209 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:38 a.m.