Triple
T17039607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Share a Coke |
E413409
|
entity |
| Predicate | relationshipTermExample |
P94755
|
FINISHED |
| Object | Mom |
—
|
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: Mom | Statement: [Share a Coke, relationshipTermExample, Mom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTermExample Context triple: [Share a Coke, relationshipTermExample, Mom]
-
A.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
B.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
C.
addressesRelationship
Indicates that one entity directs communication, remarks, or attention specifically toward another entity.
-
D.
relationshipEnd
Indicates that a previously existing relationship between entities has been terminated or has come to an end.
-
E.
relationshipToRelative
chosen
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8f5844c819097eade4a2b42ab91 |
completed | April 18, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e35d60a588819084f53ef9f8b2e7c0 |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.