Triple
T16518266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coach Klein |
E401241
|
entity |
| Predicate | relationshipTypeWithBobbyBoucher |
P123867
|
FINISHED |
| Object | coach-player relationship |
—
|
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: coach-player relationship | Statement: [Coach Klein, relationshipTypeWithBobbyBoucher, coach-player relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithBobbyBoucher Context triple: [Coach Klein, relationshipTypeWithBobbyBoucher, coach-player relationship]
-
A.
relationshipToBobinot
Indicates the nature or type of relationship that one entity has with Bobinot.
-
B.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
-
C.
relationshipToAlBundy
Indicates the specific familial, social, or personal relationship that an entity has to the person Al Bundy.
-
D.
relationshipTypeWithRobertCohn
Indicates the specific nature or category of relationship that an entity has with Robert Cohn.
-
E.
relationshipTypeWithMikaelBoghosian
Indicates the specific nature or category of relationship that an entity has with Mikael Boghosian.
- 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e7da2248190a540a5ef8686963d |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:14 a.m.