Triple
T6578446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Moulson |
E157229
|
entity |
| Predicate | relationshipToJonathanQuick |
P71596
|
FINISHED |
| Object | brothers-in-law |
—
|
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: brothers-in-law | Statement: [Matt Moulson, relationshipToJonathanQuick, brothers-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJonathanQuick Context triple: [Matt Moulson, relationshipToJonathanQuick, brothers-in-law]
-
A.
relationshipToJohnMcVay
Indicates the specific familial, professional, or social connection that an entity has to John McVay.
-
B.
relationshipToNFL
Indicates the nature or type of connection an entity has to the National Football League (NFL), such as affiliation, role, or involvement.
-
C.
relationshipToJoeKeller
Indicates the specific familial, social, or personal connection that one entity has to Joe Keller.
-
D.
associatedWithQuarterback
Indicates a relationship where an entity has a connection, role, or involvement with a quarterback.
-
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_69c6882b3a108190b3a9eb343ae4162c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6c07cdf048190945ca5810fb1de88 |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6acfb462481909cb7aff5af4bca9d |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6c07bb434819096ec3e3b966042a5 |
completed | March 27, 2026, 5:38 p.m. |
Created at: March 27, 2026, 1:54 p.m.