Triple
T38486636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faking It |
E917934
|
entity |
| Predicate | hasIntersexCharacter |
P203347
|
FINISHED |
| Object | Lauren Cooper |
—
|
NE NERFINISHED |
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: Lauren Cooper | Statement: [Faking It, hasIntersexCharacter, Lauren Cooper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIntersexCharacter Context triple: [Faking It, hasIntersexCharacter, Lauren Cooper]
-
A.
hasSexualityCharacteristic
Indicates that an entity possesses a specific sexual orientation or sexuality-related characteristic.
-
B.
hasCrossDressingProtagonist
Indicates that the main character in the work regularly dresses in clothing traditionally associated with another gender.
-
C.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
D.
hasMaleCharacterRaisedAsFemale
Indicates that a male character in the work is brought up or socialized as female, typically being treated and raised as a girl despite being male.
-
E.
hasGenderIdentity
Indicates that an entity identifies with or experiences a particular gender.
- 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_69f76e9894208190a129a553a60ca58c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a0164b052608190adc93240dad50a06 |
completed | May 11, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_6a01637fd3ac8190970eb09650d1e659 |
completed | May 11, 2026, 5:05 a.m. |
| PDg | Predicate description generation | batch_6a0164af262c8190876c9eb2421ad9a7 |
completed | May 11, 2026, 5:10 a.m. |
Created at: May 3, 2026, 4:31 p.m.