Triple
T16176774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | What I've Learned |
E392582
|
entity |
| Predicate | usesPronounPerspective |
P54869
|
FINISHED |
| Object | "I" |
—
|
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: "I" | Statement: [What I've Learned, usesPronounPerspective, "I"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesPronounPerspective Context triple: [What I've Learned, usesPronounPerspective, "I"]
-
A.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
B.
useOfPerspective
chosen
Indicates that one entity adopts or applies a particular visual, narrative, or conceptual viewpoint in relation to another entity or context.
-
C.
hasDistinctPronounsFrom
Indicates that two entities use different sets of pronouns from each other.
-
D.
usesGenderAccurateLanguage
Indicates that the language employed in the context correctly reflects and respects the gender identities of the entities referenced.
-
E.
hasPronounForYouSingular
Indicates that there is a pronoun form specifically used to address a single person as "you" in the given language or context.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e22059e7048190b4592cb1516b5f8d |
completed | April 17, 2026, 11:58 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.