Triple
T36080670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Levitation Charm |
E1043636
|
entity |
| Predicate | hasMishap |
P152793
|
FINISHED |
| Object | Can cause object to crash if concentration is lost |
—
|
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: Can cause object to crash if concentration is lost | Statement: [Levitation Charm, hasMishap, Can cause object to crash if concentration is lost]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMishap Context triple: [Levitation Charm, hasMishap, Can cause object to crash if concentration is lost]
-
A.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
B.
hasMisadventures
chosen
Indicates that an entity experiences or is involved in a series of troublesome, chaotic, or comically unfortunate events.
-
C.
hasFictionalAccident
Indicates that an entity experiences or is involved in an accident that occurs within a fictional or imagined context.
-
D.
hadIssue
Indicates that an entity experienced, encountered, or was affected by a particular problem, defect, or difficulty.
-
E.
hasDisaster
Indicates that an entity experiences, is affected by, or is associated with a disaster event.
- 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_69f76e3154908190a6f702671c2bea08 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fd49f6dbac81909744373a357b7982 |
completed | May 8, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69fd48ed68f481908374183c66a6b055 |
completed | May 8, 2026, 2:22 a.m. |
Created at: May 3, 2026, 4:08 p.m.