Triple
T19335437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brownhelm Township |
E483609
|
entity |
| Predicate | fiscalOfficerTermLength |
P39902
|
FINISHED |
| Object | four years |
—
|
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: four years | Statement: [Brownhelm Township, fiscalOfficerTermLength, four years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fiscalOfficerTermLength Context triple: [Brownhelm Township, fiscalOfficerTermLength, four years]
-
A.
termLength
Indicates the duration or period of time for which an agreement, position, or condition remains in effect.
-
B.
electsTermLength
chosen
Indicates the length of time for which an entity is elected to hold a particular position or office.
-
C.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
D.
termInOffice
Indicates the period during which an individual officially holds a particular office or position.
-
E.
termLengthNumber
Indicates the numerical value representing the duration or length of a specified term.
- 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61644b80c819080f9bca086424a36 |
completed | April 20, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
Created at: April 10, 2026, 1:33 p.m.