Triple
T16591591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christine Armstrong |
E403100
|
entity |
| Predicate | relationshipTypeWithHaydenFox |
P123441
|
FINISHED |
| Object | long-term romantic partner |
—
|
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: long-term romantic partner | Statement: [Christine Armstrong, relationshipTypeWithHaydenFox, long-term romantic partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithHaydenFox Context triple: [Christine Armstrong, relationshipTypeWithHaydenFox, long-term romantic partner]
-
A.
relationshipToHaydon
Indicates the specific type of personal or social relationship an entity has with Haydon.
-
B.
relationshipTypeWith Hank Evans
Indicates the specific nature or category of the relationship that an entity has with Hank Evans.
-
C.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
D.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
E.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
- F. None of above. chosen
Provenance (4 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e359a123e8819095cd73cd848a3345 |
completed | April 18, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69e296a7d9d0819088555bca6c936e79 |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7fb02f481908885a226c2191231 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:16 a.m.