Triple
T29164326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fear Nothing |
E739271
|
entity |
| Predicate | hasCharacterWithCondition |
P181303
|
FINISHED |
| Object | rare pain disorder |
—
|
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: rare pain disorder | Statement: [Fear Nothing, hasCharacterWithCondition, rare pain disorder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacterWithCondition Context triple: [Fear Nothing, hasCharacterWithCondition, rare pain disorder]
-
A.
containsCharacter
Indicates that one entity includes a specific character as part of its content or composition.
-
B.
containsCharacterAction
Indicates that an entity includes or features an action performed by a character within it.
-
C.
portraysCharacterWithCondition
chosen
Indicates that an entity depicts or represents a character who has a specific condition (such as a medical, psychological, or other notable state).
-
D.
hasCharacterPresence
Indicates that a particular character appears or is present within a specified context, such as a scene, work, or medium.
-
E.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
- 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_69f07cb528fc8190a556b73990c347c8 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
Created at: April 28, 2026, 11:49 a.m.