Triple
T14158899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darnell Turner |
E350884
|
entity |
| Predicate | relationshipToEarlHickey |
P113042
|
FINISHED |
| Object | 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: friend | Statement: [Darnell Turner, relationshipToEarlHickey, friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToEarlHickey Context triple: [Darnell Turner, relationshipToEarlHickey, friend]
-
A.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
B.
relationshipToHarveyCheyneJr
Indicates the specific familial, social, or professional relationship that an entity has to Harvey Cheyne Jr.
-
C.
relationshipToLucyHoneychurch
Indicates the specific type of relationship or connection an entity has to Lucy Honeychurch.
-
D.
relationshipToHollyGolightly
Indicates the nature or type of relationship an entity has with Holly Golightly.
-
E.
relationshipToHarryMonroe
Indicates the specific type of personal, social, or familial relationship that one entity has to Harry Monroe.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61377de48190a3470d28f0edd34a |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b8434c81908c33b1b513463b12 |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239a02e881909b0e2679487e4ab2 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 10, 2026, 12:58 a.m.