Triple
T12855401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bomb |
E307438
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Beat poetry |
E310170
|
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: Beat poetry | Statement: [Bomb, associatedWith, Beat poetry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beat poetry Context triple: [Bomb, associatedWith, Beat poetry]
-
A.
Zaum poetry
Zaum poetry is an experimental form of sound-based, transrational verse developed by Russian Futurist poets that emphasizes invented words and phonetic expression over conventional meaning.
-
B.
Beat literature
chosen
Beat literature is a mid-20th-century American literary movement known for its rejection of conventional values, exploration of spirituality and sexuality, and free-form, jazz-influenced prose and poetry.
-
C.
Lyrika
Lyrika is a collection of lyric poems by Greek poet Andreas Kalvos, recognized as one of his most important contributions to modern Greek literature.
-
D.
Poetry
Poetry is a Python dependency management and packaging tool that simplifies creating, building, and publishing Python projects.
-
E.
Neoteric poetry
Neoteric poetry was a Hellenistic-influenced Roman literary movement characterized by its polished style, learned allusions, and focus on personal, often playful or erotic themes rather than grand epic subjects.
- 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970231ce48190a4eabc4b8c24a3ff |
completed | April 10, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69ba9a53c81908e9ed120f6cb94af |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:37 p.m.