Triple
T11446431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Erskine-Brown |
E271277
|
entity |
| Predicate | relationshipToHoraceRumpole |
P99331
|
FINISHED |
| Object | professional rival |
—
|
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: professional rival | Statement: [Claude Erskine-Brown, relationshipToHoraceRumpole, professional rival]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHoraceRumpole Context triple: [Claude Erskine-Brown, relationshipToHoraceRumpole, professional rival]
-
A.
relationshipToDrWatson
Indicates the specific personal or professional relationship an entity has with Dr. Watson.
-
B.
relationshipToHenry
Indicates the specific type of relationship or connection that an entity has to Henry.
-
C.
relationshipToHollyGolightly
Indicates the nature or type of relationship an entity has with Holly Golightly.
-
D.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
E.
relationshipStatusWithDrEvil
Indicates the type or state of a person's relationship with Dr. Evil.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d81c6d4890819082fb4a670feb2629 |
completed | April 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69d7e7162b288190a0bfb89f7eb747c7 |
completed | April 9, 2026, 5:51 p.m. |
| PDg | Predicate description generation | batch_69d800115af08190bba53dd3ff561ca1 |
completed | April 9, 2026, 7:37 p.m. |
Created at: April 8, 2026, 9:35 p.m.