Triple
T28266915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis St. Laurent |
E712728
|
entity |
| Predicate | ordinal number in office |
P2953
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Louis St. Laurent, ordinal number in office, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ordinal number in office Context triple: [Louis St. Laurent, ordinal number in office, 12]
-
A.
ordinalNumber
Indicates the position or rank of an entity within an ordered sequence (e.g., first, second, third).
-
B.
ordinalInOffice
chosen
Indicates the numerical order or rank of an individual’s term or tenure in a particular office or position.
-
C.
numberingType
Indicates the scheme or style used to assign sequential numbers or labels within an ordered set.
-
D.
usedOrdinal
Indicates that one entity is used as an ordinal indicator or position marker relative to another entity.
-
E.
orderOf
Indicates that one entity is arranged, ranked, or sequenced before or after another according to a specified ordering criterion.
- 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_69efb5216c6881908020dce4aea65381 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6441de12c8190a2688dfca9a99326 |
completed | May 2, 2026, 6:36 p.m. |
| PD | Predicate disambiguation | batch_69f641e0fde08190bf06a1c5b388aa84 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 11:15 p.m.