Triple
T20193461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bulbasaur |
E493025
|
entity |
| Predicate | hasGenderRatioFemale |
P139120
|
FINISHED |
| Object | 12.5% |
—
|
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: 12.5% | Statement: [Bulbasaur, hasGenderRatioFemale, 12.5%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderRatioFemale Context triple: [Bulbasaur, hasGenderRatioFemale, 12.5%]
-
A.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
B.
memberCountFemale
Indicates the number of female members associated with a given group or entity.
-
C.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
D.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
E.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad7270081908ed8513a8363e9b1 |
completed | April 20, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e55b11124c8190babacf2a0fe2d057 |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56700b1a08190ace53cf95827d72d |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:37 p.m.