Triple
T19008771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lacie Pound |
E465160
|
entity |
| Predicate | relationshipToNaomiBlestow |
P59953
|
FINISHED |
| Object | childhood friend |
—
|
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: childhood friend | Statement: [Lacie Pound, relationshipToNaomiBlestow, childhood friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToNaomiBlestow Context triple: [Lacie Pound, relationshipToNaomiBlestow, childhood friend]
-
A.
relationshipToNaomi
chosen
Indicates the specific familial, social, or interpersonal connection that an entity has with Naomi.
-
B.
relationshipToNicole
Indicates the specific type of relationship or connection that an entity has with Nicole.
-
C.
relationshipToSamanthaGrimm
Indicates the specific type of relationship or connection an entity has to Samantha Grimm.
-
D.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
-
E.
relationshipToNatalieGoodman
Indicates the specific type of personal or social relationship an entity has with Natalie Goodman.
- 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6a72aa88190a04f13cd14ee77d4 |
completed | April 20, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69e4a2f88e0c81908cb20f08bf24cd32 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:02 p.m.