Triple
T3503380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Royal Trux |
E74018
|
entity |
| Predicate | hasStudioAlbum |
P48584
|
FINISHED |
| Object | Thank You |
E363869
|
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: Thank You | Statement: [Royal Trux, hasStudioAlbum, Thank You]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thank You Context triple: [Royal Trux, hasStudioAlbum, Thank You]
-
A.
Thank You
"Thank You" is a song featured on the jazz standard album "All of Me."
-
B.
Thank You
chosen
"Thank You" is a 1995 studio album by the American rock band Royal Trux, known for its experimental, lo-fi blend of noise rock and classic rock influences.
-
C.
Thank You So Much
"Thank You So Much" is a lesser-known song composed by Richard Rodgers, the influential American composer famed for his work in musical theatre.
-
D.
Thank You for the Music
"Thank You for the Music" is a popular ABBA song, later featured prominently in the musical and film "Mamma Mia!" where it is performed by the character Harry Bright among others.
-
E.
I Wanna Thank Me
"I Wanna Thank Me" is a 2019 studio album by Snoop Dogg that showcases his veteran West Coast hip-hop style and self-reflective celebration of his long career.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf0c2b48190b49923137bb9e45d |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e6aea8c81908dbe4748dd8a9d9c |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:18 p.m.