Triple
T22591814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bebo |
E564967
|
entity |
| Predicate | userRelationshipModel |
P148848
|
FINISHED |
| Object | mutual friends |
—
|
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: mutual friends | Statement: [Bebo, userRelationshipModel, mutual friends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: userRelationshipModel Context triple: [Bebo, userRelationshipModel, mutual friends]
-
A.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
addressesRelationship
Indicates that one entity directs communication, remarks, or attention specifically toward another entity.
-
C.
playerRelations
Indicates the nature or status of the relationship between players, such as alliances, rivalries, or other interpersonal dynamics.
-
D.
relationshipToFriends
Indicates the type or nature of the relationship an entity has with its friends.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f16162c2cc8190a506776ac52356d7 |
completed | April 29, 2026, 1:39 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 2:49 p.m.