Triple
T2699516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugene V. Debs |
E59195
|
entity |
| Predicate | numberOfPresidentialCampaigns |
P42054
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Eugene V. Debs, numberOfPresidentialCampaigns, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPresidentialCampaigns Context triple: [Eugene V. Debs, numberOfPresidentialCampaigns, 5]
-
A.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
B.
numberOfPresidents
Indicates the total count of individuals who have held the position of president for a given entity or within a specified context.
-
C.
wonPresidencyWith
Indicates that one entity attained the presidency by means of, or through the support, strategy, or circumstances provided by, another entity.
-
D.
ranPresidentialCandidate
Indicates that the subject has been a candidate in a presidential election.
-
E.
presidentialNumber
Indicates the ordinal position a person holds in a sequence of presidents (e.g., first, second, third president).
- 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_69ab4ac66bc88190b9e4afa5fc843f72 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda34ba508190be8e2c9e4052adfc |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd82062988190b4292f242ad70b2c |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd9ceec708190aa162399023b2273 |
completed | March 7, 2026, 7:54 a.m. |
Created at: March 6, 2026, 9:55 p.m.