Triple
T2914462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MTV Moon Person |
E63765
|
entity |
| Predicate | genderNeutralNameAdopted |
P42893
|
FINISHED |
| Object | 2017 |
—
|
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: 2017 | Statement: [MTV Moon Person, genderNeutralNameAdopted, 2017]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderNeutralNameAdopted Context triple: [MTV Moon Person, genderNeutralNameAdopted, 2017]
-
A.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
B.
namedForGender
Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
-
C.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
D.
adoptiveNomen
Indicates a naming relationship where one entity has taken on or been given the name of another in an adoptive or non-biological sense.
-
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_69ab4c44ab448190b9411324e8a1fc1d |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe0edb1ac81908b22ef60abb4a4df |
completed | March 7, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69abdd1b77608190b20fc078fdb85e64 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddedd72c819094a9c4161af07780 |
completed | March 7, 2026, 8:12 a.m. |
Created at: March 6, 2026, 10:11 p.m.