Triple
T26318954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery, Pennsylvania |
E662048
|
entity |
| Predicate | hasMotherOfUSPresidentBornHere |
P175204
|
FINISHED |
| Object | Ulysses S. Grant |
—
|
NE NERFINISHED |
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: Ulysses S. Grant | Statement: [Montgomery, Pennsylvania, hasMotherOfUSPresidentBornHere, Ulysses S. Grant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotherOfUSPresidentBornHere Context triple: [Montgomery, Pennsylvania, hasMotherOfUSPresidentBornHere, Ulysses S. Grant]
-
A.
hasHighestOfficeHolderBornHere
Indicates that the highest-ranking office holder of a given political or administrative unit was born in the referenced place.
-
B.
hometownOfHonoree
Indicates that a specified place is the hometown (place of origin or long-term residence) of the honoree.
-
C.
homeOfPresident
Indicates the location that serves as the residence of a president.
-
D.
hasNotablePersonBornHere
Indicates that a notable or distinguished person was born at the referenced place.
-
E.
locatedInFormerPresidency
Indicates that an entity was geographically or administratively situated within a territory that was formerly governed as a presidency.
- 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_69ee812e73048190aae587f1d51e5a06 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f6cee547108190ad3bc84297d8f516 |
completed | May 3, 2026, 4:28 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1188708190b8f0f56e595e6057 |
completed | May 3, 2026, 4:16 a.m. |
| PDg | Predicate description generation | batch_69f6cee3604c81908a07eade2f39064e |
completed | May 3, 2026, 4:28 a.m. |
Created at: April 26, 2026, 10:27 p.m.