Triple
T30396763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | $100,000 infield |
E773239
|
entity |
| Predicate | hasFamePeriod |
P105129
|
FINISHED |
| Object | 1910–1914 |
—
|
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: 1910–1914 | Statement: [$100,000 infield, hasFamePeriod, 1910–1914]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamePeriod Context triple: [$100,000 infield, hasFamePeriod, 1910–1914]
-
A.
hadPeakInfluencePeriod
Indicates the time span during which an entity exerted its greatest or most significant influence.
-
B.
notablePopularityPeriod
chosen
Indicates the time span during which something was especially popular or widely recognized.
-
C.
famousAt
Indicates that an entity is widely known or recognized in a particular place, context, or time.
-
D.
usesPastFameFor
Indicates that one entity leverages or exploits its previous fame or reputation to gain influence, advantage, or benefit in relation to another entity.
-
E.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
- 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_69f2248facd48190b183c3f3ca6daef7 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: April 29, 2026, 8:02 p.m.