Triple
T10604988
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siobhan "Shiv" Roy |
E275848
|
entity |
| Predicate | relationshipWithTomWambsgans |
P94884
|
FINISHED |
| Object | complex and often adversarial |
—
|
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: complex and often adversarial | Statement: [Siobhan "Shiv" Roy, relationshipWithTomWambsgans, complex and often adversarial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipWithTomWambsgans Context triple: [Siobhan "Shiv" Roy, relationshipWithTomWambsgans, complex and often adversarial]
-
A.
relationshipWithTomRogan
Indicates that an entity has some form of relationship or association with Tom Rogan.
-
B.
relationshipToTony
Indicates the specific type of relationship or connection that an entity has with Tony.
-
C.
relationshipWithTobyFlenderson
Indicates that one entity has some form of relationship, connection, or association with Toby Flenderson.
-
D.
relationshipToGabeGoodman
Indicates the specific type of personal or social relationship an entity has with Gabe Goodman.
-
E.
relationshipToEvanHansen
Indicates the type or nature of a person's relationship or connection to Evan Hansen.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4a5df88190b993196ca7849a88 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 7:32 p.m.