Triple
T34896720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Connelly |
E1006454
|
entity |
| Predicate | hasRelationshipTypeWithHollyKennedy |
P205598
|
FINISHED |
| Object | emotional support |
—
|
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: emotional support | Statement: [Daniel Connelly, hasRelationshipTypeWithHollyKennedy, emotional support]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithHollyKennedy Context triple: [Daniel Connelly, hasRelationshipTypeWithHollyKennedy, emotional support]
-
A.
hasRelationshipTypeWithRoryGilmore
Indicates that an entity has a specific type of interpersonal relationship or connection with Rory Gilmore.
-
B.
hasRelationshipTypeWith Owen Hunt
Indicates that there exists a specific type of interpersonal or relational connection between an entity and Owen Hunt.
-
C.
hasRelationshipTypeWithJenniferHart
Indicates that an entity has a specific, defined type of relationship with Jennifer Hart.
-
D.
hasRelationshipTypeWithJoelKnox
Indicates that an entity has a specific type of relationship or association with Joel Knox.
-
E.
hasRelationshipTypeWithDrewCarey
Indicates that an entity has a specific type of interpersonal or professional relationship with Drew Carey.
- 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_69f76dbfe5788190ad8b64f241f470c8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4 p.m.