Triple
T31567645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Brown |
E805461
|
entity |
| Predicate | hasPersonalRelationshipWith |
P61561
|
FINISHED |
| Object | Tiffy Gerhardt |
—
|
NE NERFINISHED |
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: Tiffy Gerhardt | Statement: [Kim Brown, hasPersonalRelationshipWith, Tiffy Gerhardt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPersonalRelationshipWith Context triple: [Kim Brown, hasPersonalRelationshipWith, Tiffy Gerhardt]
-
A.
influencedByPersonalRelationshipWith
Indicates that one entity’s decisions, opinions, or actions are shaped or affected by a personal relationship it has with another entity.
-
B.
haveRelationshipWith
Indicates that one entity is in some form of defined relationship or association with another entity.
-
C.
inRelationshipWith
chosen
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
D.
worksInCloseRelationshipWith
Indicates a collaborative professional relationship in which two or more entities work together closely and interact frequently to achieve shared goals.
-
E.
hasFamilialTieTo
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
- 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_69f348d2ee94819091918d1789398c29 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd35d108908190b79b1e8e6bbd62aa |
completed | May 8, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fd34cb46108190b43c3b7f67ec4cd4 |
completed | May 8, 2026, 12:56 a.m. |
Created at: April 30, 2026, 10:18 p.m.