Triple
T29150211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Doggett |
E738885
|
entity |
| Predicate | relationshipWithDanaScully |
P202956
|
FINISHED |
| Object | professional partner |
—
|
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 partner | Statement: [John Doggett, relationshipWithDanaScully, professional partner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithDanaScully Context triple: [John Doggett, relationshipWithDanaScully, professional partner]
-
A.
relationshipToJaneRizzoli
Indicates the specific familial, social, or professional relationship that one entity has to Jane Rizzoli.
-
B.
relationshipToShawnSpencer
Indicates the specific type of personal or social relationship an entity has with Shawn Spencer.
-
C.
relationshipWithKateBeckett
Indicates that there exists a personal or professional relationship involving Kate Beckett and another entity.
-
D.
relationshipToWalterBurns
Indicates the specific nature of the relationship an entity has with Walter Burns, such as familial, professional, or social connection.
-
E.
relationshipToSelinaMeyer
Indicates the specific type of personal or professional relationship an entity has with Selina Meyer.
- 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_69f07cb46f148190874eb8576a447567 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_6a00d6aff0a08190a330dd0c7c1352f9 |
completed | May 10, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_6a00d60f2a508190aeaf5a9d8af9c39e |
completed | May 10, 2026, 7:01 p.m. |
| PDg | Predicate description generation | batch_6a00d6af4c588190a52f727a9dabd3dc |
completed | May 10, 2026, 7:04 p.m. |
Created at: April 28, 2026, 11:41 a.m.