Triple
T2210097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles de Gaulle |
E50893
|
entity |
| Predicate | totalPersonnel |
P24266
|
FINISHED |
| Object | around 1,800 |
—
|
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: around 1,800 | Statement: [Charles de Gaulle, totalPersonnel, around 1,800]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalPersonnel Context triple: [Charles de Gaulle, totalPersonnel, around 1,800]
-
A.
personnelComposition
Indicates the makeup or distribution of people or roles within a group, organization, or unit.
-
B.
personnelStrength
chosen
Indicates the number or capacity of people assigned to or available for a particular unit, organization, or operation.
-
C.
nationalityOfPersonnel
Indicates the country or countries to which the personnel involved in an activity, organization, or context belong by citizenship or national affiliation.
-
D.
totalCrewMembers
Indicates the total number of crew members associated with a given entity or context.
-
E.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.