Triple
T8665810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Randolph |
E205670
|
entity |
| Predicate | hasSexualityCharacteristic |
P83966
|
FINISHED |
| Object | ambiguous |
—
|
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: ambiguous | Statement: [Randolph, hasSexualityCharacteristic, ambiguous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSexualityCharacteristic Context triple: [Randolph, hasSexualityCharacteristic, ambiguous]
-
A.
hasSex
Indicates that one entity engages in sexual activity with another entity.
-
B.
hasSexPredominance
Indicates that one sex (male or female) is more commonly or predominantly associated with the given condition, trait, or occurrence than the other.
-
C.
hasAsexualMorph
Indicates that an entity possesses or is associated with an asexual morphological form in its life cycle.
-
D.
coneSex
Indicates a sexual or mating relationship involving a cone-shaped structure or entity.
-
E.
depictsSex
Indicates that one entity visually represents or portrays sexual activity or sexual content involving another entity.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc48a1dd1481908c56abca48fcd562 |
completed | March 31, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc46c330bc8190a9b644078881c6ff |
completed | March 31, 2026, 10:12 p.m. |
Created at: March 30, 2026, 6:31 p.m.