Triple
T15997679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Del Griffith |
E388013
|
entity |
| Predicate | relationshipToNealPage |
P121221
|
FINISHED |
| Object | travel companion |
—
|
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: travel companion | Statement: [Del Griffith, relationshipToNealPage, travel companion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToNealPage Context triple: [Del Griffith, relationshipToNealPage, travel companion]
-
A.
relationshipToPaul
Indicates a specified type of personal or social relationship that an entity has with Paul.
-
B.
relationshipToNicole
Indicates the specific type of relationship or connection that an entity has with Nicole.
-
C.
relationshipToGeorgePage
Indicates the specific familial, social, or professional relationship that an entity has to George Page.
-
D.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
E.
relationshipToNedSchneebly
Indicates the specific type of personal, social, or professional relationship an entity has with Ned Schneebly.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4e871c819082d7b1c1eaf5b4fe |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d9d8e881909b559a3e3ca21d24 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e17d48cc9c8190b03fd07ae2e9dfd8 |
completed | April 17, 2026, 12:22 a.m. |
Created at: April 10, 2026, 4:55 a.m.