Triple
T37971248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | People v. Balfour |
E947290
|
entity |
| Predicate | relationshipToJenniferHudson |
P204501
|
FINISHED |
| Object | brother-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: brother-in-law | Statement: [People v. Balfour, relationshipToJenniferHudson, brother-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJenniferHudson Context triple: [People v. Balfour, relationshipToJenniferHudson, brother-in-law]
-
A.
hasRelationshipTypeWithJenniferHart
Indicates that an entity has a specific, defined type of relationship with Jennifer Hart.
-
B.
relationshipToEvanHansen
Indicates the type or nature of a person's relationship or connection to Evan Hansen.
-
C.
relationshipToJeanieDeans
Indicates the specific type of relationship or connection an entity has to Jeanie Deans.
-
D.
relationshipToAmyJuergens
Indicates the specific interpersonal or familial connection that an entity has to Amy Juergens.
-
E.
relationshipTypeWith Alicia Johns
Indicates the specific type or nature of the relationship that an entity has with Alicia Johns.
- 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_69f76ef7db908190bba6086673a32300 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:20 p.m.