Triple
T14702785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darling |
E345347
|
entity |
| Predicate | relationshipToScamp |
P96394
|
FINISHED |
| Object | owner |
—
|
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: owner | Statement: [Darling, relationshipToScamp, owner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToScamp Context triple: [Darling, relationshipToScamp, owner]
-
A.
relationshipToEvent
Indicates the specific way an entity is connected or related to a particular event.
-
B.
relationshipToCarmen
Indicates the specific type of personal or social relationship an entity has with Carmen.
-
C.
relationshipToCreature
chosen
Indicates a specified type of relational connection that one entity has toward a particular creature.
-
D.
relationshipToPawneeNation
Indicates the specific type of familial, legal, historical, or political relationship that an entity has with the Pawnee Nation.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb6071e5c8190bb5509c859135c2d |
completed | April 14, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.