Triple
T33877946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caroline Helstone |
E868405
|
entity |
| Predicate | relationshipToShirleyKeeldar |
P206713
|
FINISHED |
| Object | close friend and emotional contrast |
—
|
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: close friend and emotional contrast | Statement: [Caroline Helstone, relationshipToShirleyKeeldar, close friend and emotional contrast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToShirleyKeeldar Context triple: [Caroline Helstone, relationshipToShirleyKeeldar, close friend and emotional contrast]
-
A.
relationshipToShireen
Indicates the specific type of personal or social relationship that an entity has with Shireen.
-
B.
relationToShannara
Indicates a relationship or connection that an entity has to the Shannara universe, works, or related elements.
-
C.
relationshipToSheilaGreene
Indicates the specific type of relationship or connection an entity has to Sheila Greene.
-
D.
relationshipToDonnaSheridan
Indicates the specific type of personal or familial connection an entity has with Donna Sheridan.
-
E.
relationshipWithKlyden
Indicates a relationship or connection that an entity has specifically with Klyden.
- 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_69f34995b81c8190acdb45cea5a10eff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037e0953908190b2930b3c06a40129 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a0379f6c3308190b954f7810214ceed |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037e07fe4481909ca21eae7a941ee7 |
completed | May 12, 2026, 7:22 p.m. |
Created at: May 1, 2026, 1:48 a.m.