Triple
T22103516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Guerre |
E546226
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | “How Many Tears?” |
—
|
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: “How Many Tears?” | Statement: [Martin Guerre, notableSong, “How Many Tears?”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “How Many Tears?” Context triple: [Martin Guerre, notableSong, “How Many Tears?”]
-
A.
“How Many Tears?”
chosen
“How Many Tears?” is a song associated with the musical adaptation of the Martin Guerre story, reflecting the production’s dramatic and emotional themes.
-
B.
“Many Tears Ago”
“Many Tears Ago” is a song featured on the classic pop standard album *Sentimental Journey*.
-
C.
Too Many Tears
"Too Many Tears" is a country song recorded by American band Restless Heart, known for their smooth harmonies and melodic, radio-friendly sound.
-
D.
So Many Tears
"So Many Tears" is an R&B/soul song by American singer Regina Belle, showcasing her smooth vocals and emotive, romantic style.
-
E.
So Many Tears
"So Many Tears" is a reflective and emotionally charged song by Tupac Shakur that explores themes of pain, loss, and inner turmoil.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.