Triple
T4685029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruby (character in "Ruby, Don’t Take Your Love to Town") |
E103899
|
entity |
| Predicate | roleInSong |
P24740
|
FINISHED |
| Object | titular woman |
—
|
LITERAL 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: titular woman | Statement: [Ruby (character in "Ruby, Don’t Take Your Love to Town"), roleInSong, titular woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInSong Context triple: [Ruby (character in "Ruby, Don’t Take Your Love to Town"), roleInSong, titular woman]
-
A.
musicalRole
chosen
Indicates the specific function or part an entity performs within a musical context, such as in a performance, composition, or ensemble.
-
B.
roleInRhyme
Indicates the specific function or part an entity plays within a rhyme, such as a character, object, or structural element of the rhyming text.
-
C.
playsInRole
Indicates that an entity performs or appears in a specific role within a production, event, or context.
-
D.
roleInScene
Indicates that an entity participates in a particular scene with a specific role or function within that scene.
-
E.
roleInDialogue
Indicates that an entity participates in a dialogue with a specific conversational role (e.g., speaker, listener, moderator) relative to other participants.
- F. None of above.
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_69bd43debbf08190b4bc372e286ec234 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6217e0088190836570522e324dc6 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:16 p.m.