Triple
T6176160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Rachel Maddow Show |
E137823
|
entity |
| Predicate | hasHostSexualOrientation |
P4324
|
FINISHED |
| Object | lesbian |
—
|
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: lesbian | Statement: [The Rachel Maddow Show, hasHostSexualOrientation, lesbian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHostSexualOrientation Context triple: [The Rachel Maddow Show, hasHostSexualOrientation, lesbian]
-
A.
hasHostGender
Indicates that an entity has or is associated with a specific gender of its host.
-
B.
sexualOrientation
chosen
Indicates an entity’s enduring pattern of romantic or sexual attraction toward others, typically in terms of the genders or sexes to which it is attracted.
-
C.
sexualOrientationRevealedIn
Indicates that an entity’s sexual orientation is disclosed, made known, or becomes apparent within a specified context, medium, or situation.
-
D.
hasSex
Indicates that one entity engages in sexual activity with another entity.
-
E.
hasSexPredominance
Indicates that one sex (male or female) is more commonly or predominantly associated with the given condition, trait, or occurrence than the other.
- F. None of above.
Provenance (3 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dc70ac481909fd1db0d69837eca |
completed | March 22, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c055f7f12881908e21c04e9b752ba4 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:18 p.m.