Triple
T32560226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mars Blackmon (She’s Gotta Have It TV series) |
E832203
|
entity |
| Predicate | relationshipToNola |
P206310
|
FINISHED |
| Object | on-and-off boyfriend |
—
|
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: on-and-off boyfriend | Statement: [Mars Blackmon (She’s Gotta Have It TV series), relationshipToNola, on-and-off boyfriend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToNola Context triple: [Mars Blackmon (She’s Gotta Have It TV series), relationshipToNola, on-and-off boyfriend]
-
A.
relationshipToParis
Indicates the specific type of connection or association an entity has with Paris.
-
B.
relationshipToPawneeNation
Indicates the specific type of familial, legal, historical, or political relationship that an entity has with the Pawnee Nation.
-
C.
relationshipToAlice
Indicates the specific type of relationship or connection that an entity has with Alice.
-
D.
relationshipToNina
Indicates that one entity has a specified personal or social relationship to Nina.
-
E.
relationshipToRelative
Indicates the specific familial connection or kinship role that one person has in relation to a particular relative.
- 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_69f34926b9848190ace47d2dd0a0de7c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:03 a.m.