Triple
T242503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Becoming |
E4962
|
entity |
| Predicate | narrativePeriodCovered |
P3058
|
FINISHED |
| Object | Michelle Obama's childhood |
—
|
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: Michelle Obama's childhood | Statement: [Becoming, narrativePeriodCovered, Michelle Obama's childhood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativePeriodCovered Context triple: [Becoming, narrativePeriodCovered, Michelle Obama's childhood]
-
A.
timePeriodCoveredTo
chosen
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
-
B.
refersToPeriod
Indicates that one entity designates, references, or is associated with a specific time period or interval.
-
C.
containsNarrativeOf
Indicates that one entity includes or presents the story, account, or narrative content of another entity.
-
D.
narrativeFrame
Indicates the overarching narrative context or perspective within which events, actions, or relationships are presented or interpreted.
-
E.
livedDuring
Indicates that the subject existed or was alive at some point within the time period or lifespan associated with the object.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b60ad308190b12f119960a8bde7 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.