Triple
T824990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruby, Don’t Take Your Love to Town |
E17833
|
entity |
| Predicate | narratorRole |
P20006
|
FINISHED |
| Object | disabled veteran husband |
—
|
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: disabled veteran husband | Statement: [Ruby, Don’t Take Your Love to Town, narratorRole, disabled veteran husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narratorRole Context triple: [Ruby, Don’t Take Your Love to Town, narratorRole, disabled veteran husband]
-
A.
narratorOf
Indicates that one entity serves as the narrator or storytelling voice for another entity, such as a text, story, or media work.
-
B.
fictionalNarrator
Indicates that one entity serves as the narrator or storytelling voice within a fictional work that features the other entity.
-
C.
sectionNarrator
Indicates that a given entity serves as the narrator or narrative voice for a particular section of a work.
-
D.
narratedTo
Indicates that one entity tells or recounts a story, event, or information directly to another entity as the audience.
-
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. chosen
Provenance (4 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ab7eb0a08190889463edb0e7bd59 |
completed | March 1, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69a4aa781e1081909df006f730296c53 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab4781c88190ae36906251347cdc |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:38 p.m.