Triple
T24144607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Earl Christian Campbell |
E598340
|
entity |
| Predicate | notableNicknamedAfter |
P38200
|
FINISHED |
| Object | hometown of Tyler, Texas |
—
|
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: hometown of Tyler, Texas | Statement: [Earl Christian Campbell, notableNicknamedAfter, hometown of Tyler, Texas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableNicknamedAfter Context triple: [Earl Christian Campbell, notableNicknamedAfter, hometown of Tyler, Texas]
-
A.
notableNickname
Indicates that one entity is a well-known or widely recognized nickname or moniker for another entity.
-
B.
notableAsNamesakeOf
Indicates that one entity is recognized or distinguished specifically for being the namesake of another entity.
-
C.
nicknamedFor
chosen
Indicates that one entity serves as the source, inspiration, or reason for another entity’s nickname.
-
D.
notablePlaceNamedAfter
Indicates that a notable place (such as a city, building, or landmark) is named in honor of or derived from the name of a particular entity.
-
E.
namedPersonNotableFor
Indicates that a person is especially known or recognized for a particular work, role, achievement, or characteristic.
- 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_69e288c9e488819093dd1acd91b08b8a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e008efbc8190ac6c12d3ba5dd5d8 |
completed | April 29, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11:29 p.m.