Triple
T33567377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borough of Princeton |
E859793
|
entity |
| Predicate | hadHistoricRole |
P29914
|
FINISHED |
| Object | center of the town of Princeton |
—
|
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: center of the town of Princeton | Statement: [Borough of Princeton, hadHistoricRole, center of the town of Princeton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadHistoricRole Context triple: [Borough of Princeton, hadHistoricRole, center of the town of Princeton]
-
A.
hasHistoricalRoleAs
chosen
Indicates that an entity has served in a specific historical capacity, function, or position during a particular period or context.
-
B.
hasObscureHistoricalRole
Indicates that an entity plays a minor, little-known, or poorly documented role in historical events or contexts.
-
C.
hasNamesakeRoleInHistory
Indicates that an entity has a historical role or position that is the same as, or named after, another entity’s role in history.
-
D.
hasHistoricalActor
Indicates that an entity is associated with or involves a specific historical person as a participant or subject.
-
E.
mayHavePriorRole
Indicates that an entity is allowed or expected to have held a specified role at some earlier time.
- 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_69f3497c1d288190a844ea699914e038 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f740ee548190a73daee3321d8264 |
completed | May 3, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:40 a.m.