Triple
T16068511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honey Fitz |
E389797
|
entity |
| Predicate | cause of fame |
P43479
|
FINISHED |
| Object | leadership in Boston city politics |
—
|
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: leadership in Boston city politics | Statement: [Honey Fitz, cause of fame, leadership in Boston city politics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cause of fame Context triple: [Honey Fitz, cause of fame, leadership in Boston city politics]
-
A.
fameFor
Indicates that one entity is widely known or recognized specifically because of, or in connection with, another entity.
-
B.
possibleCauseOfNotability
Indicates that one entity is a potential reason or contributing factor for why another entity is notable or recognized.
-
C.
helpedPropelToMainstreamFame
Indicates that one entity significantly contributed to another entity’s rise to widespread public recognition or mainstream popularity.
-
D.
claimToFame
chosen
Indicates the notable achievement, characteristic, or association for which an entity is best known.
-
E.
starMadeFamous
Indicates that one entity (such as a work, event, or role) is what caused another entity (typically a person) to become widely known or famous.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e18272f2288190a17d45fb01cc2b07 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:57 a.m.