Triple
T19915005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | writing the novel "Watership Down" |
E478641
|
entity |
| Predicate | hasToneInResult |
P53965
|
FINISHED |
| Object | serious |
—
|
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: serious | Statement: [writing the novel "Watership Down", hasToneInResult, serious]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasToneInResult Context triple: [writing the novel "Watership Down", hasToneInResult, serious]
-
A.
hasEndingTone
chosen
Indicates that something concludes with a particular tone, mood, or intonational quality.
-
B.
supportsTone
Indicates that one entity is compatible with, enables, or can correctly handle a specified tone or tonal characteristic of another entity.
-
C.
hasPhonemicTone
Indicates that a language, word, or syllable uses pitch differences (tones) as phonemic contrasts that can change meaning.
-
D.
hasLiturgicalTone
Indicates that something (such as a chant, text, or musical phrase) is associated with or performed in a specific liturgical tone or mode.
-
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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6599394f081909246006c2e83bacc |
completed | April 20, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:53 p.m.