Triple
T33943876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 Free Way to the Catalan Republic rally |
E870237
|
entity |
| Predicate | hasNonViolentCharacter |
P187271
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [2015 Free Way to the Catalan Republic rally, hasNonViolentCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNonViolentCharacter Context triple: [2015 Free Way to the Catalan Republic rally, hasNonViolentCharacter, true]
-
A.
isNonViolent
chosen
Indicates that an entity refrains from using physical force or aggression in behavior, actions, or interactions.
-
B.
hasNotableCharacterDynamic
Indicates that there is a particularly distinctive, memorable, or significant pattern of interaction or relationship between the involved characters.
-
C.
includesNonHumanCharacters
Indicates that the subject contains or features characters that are not human, such as animals, aliens, or other non-human entities.
-
D.
containsViolence
Indicates that the subject includes, depicts, or involves acts of physical harm, aggression, or violent behavior.
-
E.
hasHumanCharacters
Indicates that the subject includes or features characters that are human beings.
- 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_69f3499b0dd48190b07b4b60babcee02 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a0226c8b2f881909de3c1a5ba40fc49 |
completed | May 11, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_6a0225602acc8190978ab4aacc615b8d |
completed | May 11, 2026, 6:52 p.m. |
Created at: May 1, 2026, 1:49 a.m.