Triple
T32401578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toothless |
E827964
|
entity |
| Predicate | leadCharacterOccupationBeforeDeath |
P35945
|
FINISHED |
| Object | dentist |
—
|
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: dentist | Statement: [Toothless, leadCharacterOccupationBeforeDeath, dentist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadCharacterOccupationBeforeDeath Context triple: [Toothless, leadCharacterOccupationBeforeDeath, dentist]
-
A.
roleInDeaths
Indicates the role or involvement an entity had in causing, contributing to, or being responsible for one or more deaths.
-
B.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
C.
leadActorUntilDeath
Indicates that an individual served as the lead actor in a production or series continuously up until their death.
-
D.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
E.
leadActorOccupation
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
- 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_69f34919342c8190a4c3bf35a90d4e58 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c21ae67c819089836c9d5c84e5cb |
completed | May 3, 2026, 3:33 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:52 a.m.