Triple
T6557891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murphy Cooper |
E152496
|
entity |
| Predicate | relationshipToJosephCooper |
P71787
|
FINISHED |
| Object | daughter |
—
|
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: daughter | Statement: [Murphy Cooper, relationshipToJosephCooper, daughter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJosephCooper Context triple: [Murphy Cooper, relationshipToJosephCooper, daughter]
-
A.
relationshipToBenjy
Indicates the specific type of relationship or connection an entity has to Benjy.
-
B.
relationshipToJoeKeller
Indicates the specific familial, social, or personal connection that one entity has to Joe Keller.
-
C.
relationshipToNaomi
Indicates the specific familial, social, or interpersonal connection that an entity has with Naomi.
-
D.
relationshipToSophie
Indicates the specific type of personal or social connection that an entity has to Sophie.
-
E.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
- 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_69c688058d6881908c19b309cc55dbfa |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6c1b15d3481908ae66e3d7564b352 |
completed | March 27, 2026, 5:43 p.m. |
| PD | Predicate disambiguation | batch_69c6acf6d4148190914b19e9affd8c76 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6c1b007148190b5164d6d09584cdf |
completed | March 27, 2026, 5:43 p.m. |
Created at: March 27, 2026, 1:52 p.m.