Triple
T7899918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Room in New York |
E183423
|
entity |
| Predicate | hasGenderOfCharacters |
P21355
|
FINISHED |
| Object | man and woman |
—
|
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: man and woman | Statement: [Room in New York, hasGenderOfCharacters, man and woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderOfCharacters Context triple: [Room in New York, hasGenderOfCharacters, man and woman]
-
A.
hasLeadCharacterGender
chosen
Indicates that the primary or lead character in a work has a specified gender.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
-
D.
hasGenderRole
Indicates that an entity is associated with, or expected to perform, a particular socially defined gender-based role or set of behaviors.
-
E.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a3dc2208190a6fea93b60b8daca |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92d94448190b4425bbfb64c658c |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.