Triple
T18216092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concussion (2013 film) |
E436161
|
entity |
| Predicate | featuresCharacterSexualOrientation |
P83966
|
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: [Concussion (2013 film), featuresCharacterSexualOrientation, lesbian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterSexualOrientation Context triple: [Concussion (2013 film), featuresCharacterSexualOrientation, lesbian]
-
A.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
B.
hasSexualityCharacteristic
chosen
Indicates that an entity possesses a specific sexual orientation or sexuality-related characteristic.
-
C.
genderDepicted
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
D.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
E.
leadActorSexualOrientation
Indicates the sexual orientation of the lead actor in a work or production.
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e47765a081908d0bbca1245f89ba |
completed | April 19, 2026, 2:19 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.