Triple
T5375350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rakshasas |
E108945
|
entity |
| Predicate | includesFemaleForm |
P52140
|
FINISHED |
| Object | rakshasi |
—
|
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: rakshasi | Statement: [Rakshasas, includesFemaleForm, rakshasi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesFemaleForm Context triple: [Rakshasas, includesFemaleForm, rakshasi]
-
A.
hasFeminineFormInSomeLanguages
chosen
Indicates that the referenced entity has a distinct feminine grammatical or lexical form in at least one language.
-
B.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
C.
genderedFormOf
Indicates that one term is a gender-specific variant or inflected form corresponding to another, more neutral or differently gendered term.
-
D.
hasMasculineForm
Indicates that an entity has a corresponding masculine grammatical or lexical form.
-
E.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
- 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_69bd440c77948190aad2a5f39b7b80f5 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd88801b188190b9ac35ed89167fa3 |
completed | March 20, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69bd846172788190969f24bc7503c05e |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:03 p.m.