Triple
T28964817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mona Lisas and Mad Hatters |
E732007
|
entity |
| Predicate | hasLyricSetting |
P84624
|
FINISHED |
| Object | New York City |
—
|
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: New York City | Statement: [Mona Lisas and Mad Hatters, hasLyricSetting, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricSetting Context triple: [Mona Lisas and Mad Hatters, hasLyricSetting, New York City]
-
A.
hasSettingInLyrics
chosen
Indicates that the lyrics of a work explicitly describe or reference a particular setting or location.
-
B.
hasLyricsFeature
Indicates that something possesses a particular characteristic or attribute related to its lyrics.
-
C.
hasLyric
Indicates that one entity (typically a musical work or track) contains or is associated with the lyrics provided by another entity.
-
D.
hasLyricCharacter
Indicates that a musical work or song includes a specific character or persona within its lyrics.
-
E.
hasLyricsTone
Indicates the tonal quality or emotional character expressed by the lyrics of a piece of music.
- 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_69f043ee242c8190b063248b417c5a69 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c2198208190a3954086c22cfcbf |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 28, 2026, 8:51 a.m.