Triple
T3992992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiko |
E87034
|
entity |
| Predicate | relationshipWithHumans |
P8492
|
FINISHED |
| Object | protective |
—
|
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: protective | Statement: [Kiko, relationshipWithHumans, protective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithHumans Context triple: [Kiko, relationshipWithHumans, protective]
-
A.
relationshipToHumans
chosen
Indicates the nature or type of connection, association, or relevance that something has specifically with humans.
-
B.
associatedWithAnimal
Indicates a relationship where an entity has a connection, link, or relevance to an animal.
-
C.
relationshipFocus
Indicates a relationship where particular attention, priority, or emphasis is placed on the connection between two or more entities.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
associatedBeings
Indicates that two or more beings are linked or connected to each other in some relevant way.
- 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_69aed94118148190975e6aa4e554cde9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb81040481909b22e4c445ecae0f |
completed | March 9, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69aef8f692008190bf4d637ffc3d3eaa |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.