Triple
T25665876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fran Katzenjammer |
E643513
|
entity |
| Predicate | relationshipToMannyBianco |
P192418
|
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: [Fran Katzenjammer, relationshipToMannyBianco, friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToMannyBianco Context triple: [Fran Katzenjammer, relationshipToMannyBianco, friend]
-
A.
relationshipToBianca
Indicates the specific type of personal or social relationship that one entity has with Bianca.
-
B.
relationshipToCarmen
Indicates the specific type of personal or social relationship an entity has with Carmen.
-
C.
relationshipToBenny
Indicates the specific type of personal or social relationship that an entity has with Benny.
-
D.
relationshipToMannyMachado
Indicates the specific type of relationship or connection an entity has to Manny Machado.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- 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_69e77e7e45648190a068ed3faa8016ea |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
| PDg | Predicate description generation | batch_69fd0b92150881909b1166fe6d09aa19 |
completed | May 7, 2026, 10 p.m. |
Created at: April 21, 2026, 7:04 p.m.